{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1378d2bf",
   "metadata": {},
   "source": [
    "# Transfer Learning for Time Series Forecasting with Darts\n",
    "Authors: Julien Herzen, Florian Ravasi, Guillaume Raille, Gaël Grosch.\n",
    "\n",
    "## Overview\n",
    "The goal of this notebook is to explore transfer learning for time series forecasting -- that is, training forecasting models on one time series dataset and using it on another. The notebook is 100% self-contained -- i.e., it also contains the necessary commands to install dependencies and download the datasets being used.\n",
    "\n",
    "Depending on what constitutes a \"learning task\", what we call transfer learning here can also be seen under the angle of meta-learning (or \"learning to learn\"), where models can adapt themselves to new tasks (e.g. forecasting a new time series) at inference time without further training [1].\n",
    "\n",
    "This notebook is an adaptation of a workshop on \"Forecasting and Meta-Learning\" that was given at the Applied Machine Learning Days conference in Lausanne, Switzerland, in March 2022.\n",
    "It contains the following parts:\n",
    "\n",
    "* **Part 0:** Initial setup - imports, functions to download data, etc.\n",
    "* **Part 1:** Forecasting passenger counts series for 300 airlines (`air` dataset). We will train one model per series.\n",
    "* **Part 2:** Using \"global\" models - i.e., models trained on all 300 series simultaneously.\n",
    "* **Part 3:** We will try some transfer learning, and see what happens if we train some global models on one (big) dataset (`m4` dataset) and use them on another dataset.\n",
    "* **Part 4:** We will reuse our pre-trained model(s) of Part 3 on another new dataset (`m3` dataset) and see how it compares to models specifically trained on this dataset.\n",
    "\n",
    "The compute durations written for the different models have been obtained by running the notebook on a i9-10900K CPU, with an RTX 2080s GPU, with Python 3.9.7 and Darts 0.18.0.\n",
    "\n",
    "## Part 0: Setup\n",
    "First, we need to have the right libraries and make the right imports. For the deep learning models, it helps to have a GPU, but this is not mandatory.\n",
    "\n",
    "The following two cells need to be run only once. They install the dependencies and download all the required datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf46dca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install darts==0.18.0 &> /dev/null\n",
    "!pip install xarray==0.18.2 &> /dev/null  # required to read pickle files\n",
    "!pip install xlrd==2.0.1 &> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ca559b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute this cell once to download all three datasets\n",
    "# Some datasets are are in pickle for simplicity and speed.\n",
    "!curl -L https://github.com/unit8co/amld2022-forecasting-and-metalearning/blob/main/data/m3_dataset.xls\\?raw\\=true -o m3_dataset.xls\n",
    "!curl -L https://github.com/unit8co/amld2022-forecasting-and-metalearning/blob/main/data/passengers.pkl\\?raw\\=true -o passengers.pkl\n",
    "!curl -L https://github.com/unit8co/amld2022-forecasting-and-metalearning/blob/main/data/m4_monthly_scaled.pkl\\?raw\\=true -o m4_monthly_scaled.pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a027958",
   "metadata": {},
   "source": [
    "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9689b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "from datetime import datetime\n",
    "from itertools import product\n",
    "import torch\n",
    "from torch import nn\n",
    "from typing import List, Tuple, Dict\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from sklearn.linear_model import Ridge\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.utils.losses import SmapeLoss\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.metrics import smape\n",
    "from darts.utils.utils import SeasonalityMode, TrendMode, ModelMode\n",
    "from darts.models import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5756350",
   "metadata": {},
   "source": [
    "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "826351e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "HORIZON = 18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab36bc6",
   "metadata": {},
   "source": [
    "### Datasets loading methods\n",
    "Here, we define some helper methods to load the three datasets we'll be playing with: `air`, `m3` and `m4`. \n",
    "\n",
    "All the methods below return two list of `TimeSeries`: one list of training series and one list of \"test\" series (of length `HORIZON`).\n",
    "\n",
    "For convenience, all the series are already scaled here, by multiplying each of them by a constant so that the largest value is 1. Such scaling is necessary for many models to work correctly (esp. deep learning models). It does not affect the sMAPE values, so we can evaluate the accuracy of our algorithms on the scaled series. In a real application, we would have to keep the Darts `Scaler` objects somewhere in order to inverse-scale the forecasts.\n",
    "\n",
    "If you are interested in seeing an example of how creating and scaling `TimeSeries` is done, you can inspect the function `load_m3()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff0a526b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_m3() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n",
    "    print(\"building M3 TimeSeries...\")\n",
    "\n",
    "    # Read DataFrame\n",
    "    df_m3 = pd.read_excel(\"m3_dataset.xls\", \"M3Month\")\n",
    "\n",
    "    # Build TimeSeries\n",
    "    m3_series = []\n",
    "    for row in tqdm(df_m3.iterrows()):\n",
    "        s = row[1]\n",
    "        start_year = int(s[\"Starting Year\"])\n",
    "        start_month = int(s[\"Starting Month\"])\n",
    "        values_series = s[6:].dropna()\n",
    "        if start_month == 0:\n",
    "            continue\n",
    "\n",
    "        start_date = datetime(year=start_year, month=start_month, day=1)\n",
    "        time_axis = pd.date_range(start_date, periods=len(values_series), freq=\"M\")\n",
    "        series = TimeSeries.from_times_and_values(\n",
    "            time_axis, values_series.values\n",
    "        ).astype(np.float32)\n",
    "        m3_series.append(series)\n",
    "\n",
    "    print(\"\\nThere are {} monthly series in the M3 dataset\".format(len(m3_series)))\n",
    "\n",
    "    # Split train/test\n",
    "    print(\"splitting train/test...\")\n",
    "    m3_train = [s[:-HORIZON] for s in m3_series]\n",
    "    m3_test = [s[-HORIZON:] for s in m3_series]\n",
    "\n",
    "    # Scale so that the largest value is 1\n",
    "    print(\"scaling...\")\n",
    "    scaler_m3 = Scaler(scaler=MaxAbsScaler())\n",
    "    m3_train_scaled: List[TimeSeries] = scaler_m3.fit_transform(m3_train)\n",
    "    m3_test_scaled: List[TimeSeries] = scaler_m3.transform(m3_test)\n",
    "\n",
    "    print(\n",
    "        \"done. There are {} series, with average training length {}\".format(\n",
    "            len(m3_train_scaled), np.mean([len(s) for s in m3_train_scaled])\n",
    "        )\n",
    "    )\n",
    "    return m3_train_scaled, m3_test_scaled\n",
    "\n",
    "\n",
    "def load_air() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n",
    "    # load TimeSeries\n",
    "    print(\"loading air TimeSeries...\")\n",
    "    with open(\"passengers.pkl\", \"rb\") as f:\n",
    "        all_air_series = pickle.load(f)\n",
    "\n",
    "    # Split train/test\n",
    "    print(\"splitting train/test...\")\n",
    "    air_train = [s[:-HORIZON] for s in all_air_series]\n",
    "    air_test = [s[-HORIZON:] for s in all_air_series]\n",
    "\n",
    "    # Scale so that the largest value is 1\n",
    "    print(\"scaling series...\")\n",
    "    scaler_air = Scaler(scaler=MaxAbsScaler())\n",
    "    air_train_scaled: List[TimeSeries] = scaler_air.fit_transform(air_train)\n",
    "    air_test_scaled: List[TimeSeries] = scaler_air.transform(air_test)\n",
    "\n",
    "    print(\n",
    "        \"done. There are {} series, with average training length {}\".format(\n",
    "            len(air_train_scaled), np.mean([len(s) for s in air_train_scaled])\n",
    "        )\n",
    "    )\n",
    "    return air_train_scaled, air_test_scaled\n",
    "\n",
    "\n",
    "def load_m4() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n",
    "    # load TimeSeries - the splitting and scaling has already been done\n",
    "    print(\"loading M4 TimeSeries...\")\n",
    "    with open(\"m4_monthly_scaled.pkl\", \"rb\") as f:\n",
    "        m4_series = pickle.load(f)\n",
    "\n",
    "    # filter and keep only series that contain at least 48 training points\n",
    "    m4_series = list(filter(lambda t: len(t[0]) >= 48, m4_series))\n",
    "\n",
    "    m4_train_scaled, m4_test_scaled = zip(*m4_series)\n",
    "\n",
    "    print(\n",
    "        \"done. There are {} series, with average training length {}\".format(\n",
    "            len(m4_train_scaled), np.mean([len(s) for s in m4_train_scaled])\n",
    "        )\n",
    "    )\n",
    "    return m4_train_scaled, m4_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fe37fa",
   "metadata": {},
   "source": [
    "Finally, we define a handy function to tell us how good a bunch of forecasted series are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a69b6b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_forecasts(\n",
    "    pred_series: List[TimeSeries], test_series: List[TimeSeries]\n",
    ") -> List[float]:\n",
    "\n",
    "    print(\"computing sMAPEs...\")\n",
    "    smapes = smape(test_series, pred_series)\n",
    "    plt.figure()\n",
    "    plt.hist(smapes, bins=50)\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xlabel(\"sMAPE\")\n",
    "    plt.title(\"Median sMAPE: %.3f\" % np.median(smapes))\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    return smapes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a59b4f",
   "metadata": {},
   "source": [
    "## Part 1: Local models on the `air` dataset\n",
    "\n",
    "### Inspecting Data\n",
    "\n",
    "The `air` dataset contains the number of air passengers that flew in or out of the USA per carrier (or airline company) from the year 2000 until 2019.\n",
    "\n",
    "First, we can load the train and test series by calling `load_air()` function that we have defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bca44bbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading air TimeSeries...\n",
      "splitting train/test...\n",
      "scaling series...\n",
      "done. There are 301 series, with average training length 136.83388704318938\n"
     ]
    }
   ],
   "source": [
    "air_train, air_test = load_air()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db486bff",
   "metadata": {},
   "source": [
    "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "144c3c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ax = plt.subplots(3, 2, figsize=(10, 10), dpi=100)\n",
    "\n",
    "for i, idx in enumerate([1, 20, 50, 100, 250, 300]):\n",
    "    axis = ax[i % 3, i % 2]\n",
    "    air_train[idx].plot(ax=axis)\n",
    "    axis.legend(air_train[idx].components)\n",
    "    axis.set_title(\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74829d15",
   "metadata": {},
   "source": [
    "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n",
    "\n",
    "Let's see what is the shortest train series available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4564171e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min([len(s) for s in air_train])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db3ddac",
   "metadata": {},
   "source": [
    "### A useful function to evaluate models\n",
    "\n",
    "Below, we write a small function that will make our life easier for quickly trying and comparing different local models. We loop through each serie, fit a model and then evaluate on our test dataset. \n",
    "\n",
    "> ⚠️  `tqdm` is optional and is only there to help display the training progress (as you will see it can take some time when training 300+ time series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e247f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_local_model(\n",
    "    train_series: List[TimeSeries], test_series: List[TimeSeries], model_cls, **kwargs\n",
    ") -> Tuple[List[float], float]:\n",
    "    preds = []\n",
    "    start_time = time.time()\n",
    "    for series in tqdm(train_series):\n",
    "        model = model_cls(**kwargs)\n",
    "        model.fit(series)\n",
    "        pred = model.predict(n=HORIZON)\n",
    "        preds.append(pred)\n",
    "    elapsed_time = time.time() - start_time\n",
    "\n",
    "    smapes = eval_forecasts(preds, test_series)\n",
    "    return smapes, elapsed_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a176ed58",
   "metadata": {},
   "source": [
    "### Building and evaluating models\n",
    "\n",
    "We can now try a first forecasting model on this dataset. As a first step, it is usually a good practice to see how a (very) naive model blindly repeating the last value of the training series performs. This can be done in Darts using a [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82585d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48a496f3bef0411b87a4f33328cd7ca2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/301 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive1_smapes, naive1_time = eval_local_model(air_train, air_test, NaiveSeasonal, K=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed7039d",
   "metadata": {},
   "source": [
    "So the most naive model gives us a median sMAPE of about 29.4.\n",
    "\n",
    "Can we do better with a \"less naive\" model exploiting the fact that most monthly series have a seasonality of 12?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32ad3737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99e1248113684563a05663ed93fcc603",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/301 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEVCAYAAADjHF5YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZUUlEQVR4nO3de5SkVX2v8WfLDBwUBjDihSYwOigBEiSnt4BRLqKjRoExh4siBBA94zG2noNEYzQjcLwbPKJmKeJS0JEooDETWcABRAwaXLr7SAwqyEUu01zC/X5pZvb5430Hamqquqtruqqrej+ftWpR73X/3qL6W+/semu/IeeMJGn+e8ZcFyBJ6g8DX5IKYeBLUiEMfEkqhIEvSYUw8CWpEAa+5lwIIYcQdqqfnxZCWDHXNUnzkYGvjoUQbgwhPBFCeE7T/F/Wob14Y9vIOf+PnPNHN3Y/MzHT4wohnFTP36tp/rEhhDUhhIdCCA+EEK4MIRxYL9s/hLC2Xtb4eHkH9W0WQvhaCOGmEMKD9X7/vGH54rqexv22/dAMIewRQrg8hHB/CGF147ohhL1DCBeHEO4JIdwZQjg3hPCChuVbhxC+EUL4z/px0nT1a3AY+Jqp3wNHrJsIIfwJ8My5K2fWdHRcIYQAHA3cU/+32RU55y2ArYGvAeeEELapl92ac96i6XFFB7UtAG4B9gO2Av6u3u/ipvW2btjvVB+a/wj8K/Dsep9/FUI4uF62DXA6sBjYEXgQOKNh289RvS6LgT2BvwwhvK2DY9AAMPA1UytZP+iOAb7ZuEJ9RnpKCOHmEMIddTfN5g3L3x9CuC2EcGsI4bimbc8MIXysfr5NCOG8+kzz3vr59g3rXhZC+GgI4af1me9FzWfpDes+p97+vvrs9fIQQuP7f9rjqu0DvAB4L/CWEMKmrdrLOa8Fvg5sDixptU6ncs4P55xPyjnfmHNem3M+j+oDarTLXS4Gzso5r8k5Xw/8BNitbuuCnPO5OecHcs6PAP8AvKJh24OAz+ScH8k530j1obbe/0MNLgNfM/UzYFEIYZcQwibAW4BvNa3zKeAlwB7ATsAI8BGAEMLrgb8GlgIvBl4zRVvPoDq73BHYAXiUKoAavRV4G/BcYNN6362cAKwGtgWeB3wIaBxXpJPjguqD4AfAOfX0Qa0aCyEsAN4BPARc2/YIn17/gyGE86Zbr173eVSv76+bFt1Ud9Gc0e6Dr3YqcHQIYWEIYWfg5cAlbdbdt0U7oen5H3dSt+aega9urDsbXgr8FphYt6Du8lgOHJ9zvifn/CDwCaoABTgcOCPnfFXO+WHgpHaN5Jzvzjl/rz6bfBD4OFUXRKMzcs6/yzk/ShXCe7TZ3STVmfmOOefJnPPlecOBpNoeV31szwQOA/4x5zwJfJcNu3X2DiHcB9xO1UX0Fznn++tl29X/wmh8PKs+1k/lnA9s91o01LAQOAv4Rs756nr2XcDLqD4YR4Et63XaOQ84lOoD9GrgaznnX7Roa3eqD+r3N8y+EPhgCGHLUH3Rfhzzo0uvCAvmugANpZVUfcAvZMNuj22pAmC8yn6gOgvcpH6+HTDesP5N7RqpA/ZzwOup+pYBtgwhbJJzXlNP396wySPAFm129/dUHy4X1XWdnnP+1AyOC+AvgCeB8+vps4BLQgjb5pzvrOf9LOf8yjY13Jpz3r7NsmnVXVArgSeAsXXzc84PAamevCOEMAbcFkLYsv6gbNzHs6lCe4yqL//5wHdDCHfknL/UsN5OwAXA/8w5X96wi/cCX6T6V8vdwLdp+O5Dg80zfM1Yzvkmqj7kNwD/1LT4Lqozx91yzlvXj63qLzIBbgP+sGH9HaZo6gRgZ2CvnPMiqu4FWL9LodOaH8w5n5BzfhFwMPC+EMKrZ3BcUHXnbAHcHEK4HTgXWEjVrdRT9b+cvkbVHXVI/S+Mdtb9y6XV3/eLgDU552/mnJ/MOa8GvkN1zOva2pGqi+ejOeeV6+24+lfbkTnn5+ecd6vb+HnXB6a+MvDVrbcDB9TdMk+pv6z8KvC5EMJzAUIIIyGE19WrnAMcG0LYtT6DP3GKNrak+vC4rz4znWrdKYUQDgwh7FQH5/3AGmBtp8cVQhgBXg0cSNVttAfwUuDTtL5aZ7Z9GdgFOKjuvmqsba8Qws4hhGeEEP4A+AJwWUNXUqPfVZuEt9brPx94M/Crel8jwKXAP+ScT2veOISwJITwByGETUJ1aehy4GOzeaDqHQNfXck5X59zTm0W/w1wHfCzEMIDVGeLO9fbXUD1peGl9TqXTtHMqVRXudxF9aXqhRtR8ovrOh4CrgC+lHP+UfNKUxzXXwJX5pwvyjnfvu5BFa67hxA6+eJyu7DhdfiHAIQQPhRCuKDVRvUZ9zupPmRub9j2yHqVF1G9Ng8CVwGPs/4lpqeFEE6rj+8B4L8BxwP3AlfW26wL7XfU+zupsc6GckaB/6jb+iRwZM65+UtdDajgDVAkqQye4UtSIQx8SSqEgS9JhTDwJakQgx74udvH7bff3vW2c/kYxrqHsWbrtub5XDdtDHrgd23NmjXTrzSAhrHuYawZrLufhrFmGN6625m3gS9JWp+BL0mFMPAlqRAGviQVwsCXpEIY+JJUCANfkgph4EtSIQx8SSqE97SdRsN9WdfjfQQkDRvP8CWpEAa+JBXCwJekQhj4klQIA1+SCmHgS1IhDHxJKoSBL0mFMPAlqRAGviQVwsCXpEL0bCydGOOewOeBSWACOBr4Tf0c4OMppYt71b4kaX29HDztFuCAlNKjMcZPAsuA+1NK+/ewTUlSGz0L/JTSbQ2TTwBrgS1ijD+mOssfSynd06v2JUnrC70e5jfGuCPwHWBfYFFK6e4Y49HAy1JK72mx/nJgOcDY2Njo0qVLu2p3cnKShQsXdl94bXx8vOX80dHRjd53K7NVdz8NY81g3f00jDXD8NY9MjLSclz3ngZ+jHERcB7w31NK1zTMfyZwYUpp32l20XVxExMTjIyMdLv5U/o9Hv5s1d1Pw1gzWHc/DWPNMLx1Ay2Dq2dX6cQYF1Cd2Z+cUromxrhpjHGzevE+wHW9aluStKFefml7BLAXsCLGuAL4MvCBGOPDwOPAcT1sW5LUpJdf2q4EVjbNPrtX7UmSpuYPrySpEAa+JBXCwJekQvTyS9si9fsyTknqlGf4klQIA1+SCmHgS1IhDHxJKoSBL0mFMPAlqRAGviQVwsCXpEIY+JJUCANfkgph4EtSIQx8SSqEgS9JhTDwJakQBr4kFcLAl6RCGPiSVAgDX5IKYeBLUiEMfEkqhIEvSYUw8CWpEAa+JBXCwJekQhj4klQIA1+SCmHgS1IhFvRqxzHGPYHPA5PABHA08CbgeOBR4JiU0upetS9JWl8vz/BvAQ5IKe0L3AgsA94H7A98BFjRw7YlSU1CzrnnjcQYTwaeAJaklI6r512RUnp5i3WXA8sBxsbGRpcuXdpVm5OTkyxcuLD7omvj4+Mt54+Ojs7K+s1mq+5+Gsaawbr7aRhrhuGte2RkJLSa37MunXVijDsCrwU+CGzbsGiTVuunlE4HTq8nu/40mpiYYGRkpNvNn7L99tu3nN/ug3Km6zebrbr7aRhrBuvup2GsGYa37nZ6GvgxxkXASuBYqoBf1LB4TS/bliStr5df2i4AvgOcnFK6Jsa4ENglxrgpEIFf9aptSdKGenmGfwSwF7AixrgC+DJwKnAZ8BhwTA/bliQ16Vngp5RWUnXnNDu7V21Kktrzh1eSVAgDX5IKYeBLUiEMfEkqhIEvSYUw8CWpEAa+JBXCwJekQhj4klQIA1+SCtHz4ZGHRQgth4+etfUlaa55hi9JhTDwJakQBr4kFcLAl6RCGPiSVAgDX5IK4WWZA6zdpZ855z5XImk+8Axfkgph4EtSIQx8SSqEgS9JhTDwJakQBr4kFcLAl6RCdBT4McYbYoxvbJjeL8Z4Ue/KkiTNtil/eBVjXARsAywGdowx7lAv2g94dW9LkyTNpunO8I8HbgAy8EXg9/XjRODm3pYmSZpN0w2t8DvgAuANwC+BW6nC/17gK70tTZI0m6YM/JTSt4FvxxhPBM5NKf2mP2VJkmZbp4OnnQa8I8Z4PLBJPS+nlN7eboMY41bAxcCuwN4ppatijNcCE/UqH08pXdxl3ZKkGeo08P8FiEDj8I0ZaBv4wCPAG4G/b5h3f0pp/5kUKEmaHZ0G/k7At4AvAU92skFKaRK4M8bYOHuLGOOPqc7yx1JK98ygVknSRug08FcCWwL/rw7ybr0ipXR3jPFo4GTgPc0rxBiXA8sBxsbGWLp0aVcNTU5OMjExMf2KtVWrVnXVTqc6raWx7nY1tdvX+Ph4y/mjo6Mdtd2tmb7Wg8K6+2cYa4bhrXtkZKTl/NDJzTRijA8BmwNrgUfr2TmltFUH254JnJJSuqph3jOBC1NK+06zedd3+piYmGh70K20u9nIbOn0piWNdc/0BihzdcOUmb7Wg8K6+2cYa4bhrZv1u9+f0ukZ/l1sRPgCxBg3BUJK6XFgH+C6jdmfJGlmOgr8lNLibnYeYzwf2APYGfhn4PAY48PA48Bx3exTktSdjgK/7nNvllNKK6faLqX0hqZZn+60MEnS7Oq0S+dMWnfpTBn4kqTB0Wngf4CnA38b4GjgJz2pSJLUE5324Z/SOB1j/HdgRU8q6rFeX40ziObq6h1Jg6XTPvx/adpmFFjYk4okST3RaZfOgU3TjwEfnOVaJEk91Gngv7Dh+Rrgjo38xa0kqc86usVhSukm4FXAZ4DPAkf0sihJ0uzr9J62fwd8HTisfpwRY/xQLwuTJM2ujgIfeAfwA+Al9eM86gHOJEnDodPA3wa4OKV0XUrpOqobm2zTu7IkSbOt0y9tE/CJGOOe9fQy4Be9KUmS1AudBv57qLp0jqqnr6PFWPaSpME1ZZdOjHF5jPGr9c3Ldwb+BHgp8K/AdGPZS5IGyHRn+CcA5wCklJ4Efg0QY5wA/hr4Sk+rkyTNmum+tN0BuLHF/JuBP5z1aiRJPTNd4N8FHNpi/qHAnbNfjiSpV6br0vke8N4Y46+AS+p5rwF2A77Qy8IkSbNrusD/MNUtCvcF/rhh/mX1MknSkJgy8FNKDwP7xxgPoBoSOQPjKaUf9aM4SdLs6fQGKJcCl/a4FklSD3U6tIIkacgZ+JJUCANfkgph4EtSIQx8SSpEp6NlaiOFENouyzn3sZKntatpruqR1Fue4UtSIQx8SSqEgS9JhTDwJakQPfvSNsa4FdXNzncF9k4pXRVjPAw4HngUOCaltLpX7UuS1tfLM/xHgDcC3wWIMS4A3gfsD3wEWNHDtiVJTXoW+CmlyZRS401SXgz8NqX0RErpp8DuvWpbkrSh0OtrrmOMZwKnAIuAw1NK/6ue//OU0p4t1l8OLAcYGxsbXbp0aVftTk5OsnDhwg3mj4+Pd7W/flmyZAnXX3/9nNYwOjracn6712733Xdv+VoPunbvkUE3jHUPY80wvHWPjIy0/JFNP394dR9V6K+zptVKKaXTgdPrya4/jSYmJhgZGdlg/vbbb9/tLvti1apVLFu2bE5raHcS0O61W716dcvXetC1e48MumGsexhrhuGtu51+Bv61wC4xxk2BCPyqj21LUvF6GvgxxvOpbpG4M/AV4FSq2yM+BhzTy7YlSevraeCnlN7QYvbZvWxTktSaP7ySpEIY+JJUCANfkgph4EtSIQx8SSqEgS9JhTDwJakQBr4kFcLAl6RCGPiSVAgDX5IKYeBLUiEMfEkqhIEvSYUw8CWpEAa+JBXCwJekQhj4klQIA18aQCGEtg+pWwa+JBXCwJekQhj4klQIA1+SCmHgS1IhDHxJKoSBL0mFMPC10cbHx6e8bryX15HPpF2vYVfpDHxJKoSBL0mFMPAlqRAGviQVYkE/G4sxLgZ+Afy6nnVYSunOftYgSaXqa+DXfpxSOnQO2pWkooWcc98aq8/wrwCuAy4HPpxSyk3rLAeWA4yNjY0uXbq0q7YmJydZuHDhBvPHx8e72l+/LFmyhOuvv36uy5iR2ap5dHS07bJe/H9rrHuqtufCVMe7++67t3xvD7J2f4+DbljrHhkZaXkNcr8DfzOqf1U8AnwVuCCl9L0pNum6uImJCUZGRjaYP+jXYq9atYply5bNdRkzMls1T/Ve7MX/t8a6+/l30Impjnf16tUt39uDrN3f46Ab1rqBlm+gvnbppJQeBx4HiDH+E7A3MFXgS5JmSV+v0okxbtkwuQ9V144kqQ/6/aXtK2OMH6Pq0vk9sKLP7UtSsfrdpXMBcEE/25QkVfzhlSQVwsCXpELMxQ+vpJbm8pLZdm23u1xzpusPovlwDJoZz/AlqRAGviQVwsCXpEIY+JJUCANfkgph4EtSIQx8SSrEvA388fFxQggbPKSZaPUems33UTf7n6/v7V6/1prHgS9JWp+BL0mFMPAlqRAGviQVwsCXpEIY+JJUCANfmkXtLi3sxyWHs9XGfL48cqbHNt8ugTXwJakQBr4kFcLAl6RCGPiSVAgDX5IKYeBLUiEMfEkqhIEvzXO9vj5/EIeQnuk2vV6/1/vplIEvSYUw8CWpEAa+JBXCwJekQizod4Mxxk8DfwbcCByXUprsdw2SVKK+nuHHGF8KjKSU9gGuBg7tZ/uSVLJ+d+n8GXBR/fxC4BV9bl+SitXvLp1tgNvq5/cDz25eIca4HFheT74zpXR6Nw0dfPDB5Jy7KnKuDWPdw1gzWHe/jIyMTLvObB7ToO6rn/tupd+Bfx+wqH6+FXBP8wp1wHcV8pKk9vrdpfNvwGvq568Dftrn9iWpWH0N/JTSlcAdMcbLgd2A7/WzfUkqWRi2vkBJUnf84ZUkFcLAl6RCGPiSVIi+D63QD8MyfEOMcU/g88AkMAEcDbwJOB54FDgmpbR6zgqcQozxCOALKaVtY4yHMRw17w+soDrR+QJwJ/AZYC3wrpTSf8xdda3FGJ8BfB1YAgTgHcBzGMC6Y4xbARcDuwJ7p5SuavXeiDH+EdWl1wuAFSmlH85Z0WxYN3ATsIqqvieBt6WUbhq0ursx787wh2z4hluAA1JK+1J9OC0D3gfsD3yEKpwGToxxE+Aw4JYY4wKGo+bNgROAP08pvSql9H3g48AbgbcCn57L+qawB7BZ/X7+W6rXelDrfoSqru8CTPHe+ATwduD1wP/ue5UbWq9uqhOwo+q/y08D76/nD1rdMzbvAp8hGr4hpXRbSunRevIJYGfgtymlJ1JKPwV2n7vqpnQEcC7VGeaLGY6aX051lvmDGOP3Y4wvANaklO5NKd1Mi199D4jVQIgxBqpfqj/MgNadUppMKd3ZMKvde2O7lNK1KaUHgHtijM/pe7ENmutOKT2WUrq1nnyC6n0OA1Z3N+Zj4G8DPFA/bzl8w6CJMe4IvBb4CU/XDrDJ3FTUXn12fzhwdj2r8fWGAay59jxgJ+Ag4KvAyaxf95Mxxk3norBp3EV1xnk18EXgcwxH3dD+vdGYOwP7N1q/ridRve4wJHVPZT4G/n1MM3zDIIkxLgJWAsdS9Skvali8Zi5qmsZRwDkppXVnPfcx+DVDVedPU0pPAD8E/pT1615QLxs0rwWeTCntDBwCfJbhqBvavzfWNswb5L/R04EvpZSuraeHpe625uOXtv9G1W/4TQZ8+Ia6j/M7wMkppWtijAuBXeoziwj8ak4LbG1X4E9jjEdR/ZP9PQx+zQC/AE6ou0b2AH4DvDDGuDWwJYP7xxuAu+vnd1HVumAI6ga4ltbvjdtijEuA/wSenVK6a64KbCfGeCJwQ0rp7IbZA1/3dOZd4KeUrowxrhu+4WbglLmuaQpHAHsBK2KMK4AvA6cClwGPAcfMWWVtpJT+Zt3zGGNKKb0rxvhmBrhmgJTSXTHG7wM/BjJwHDACnF9P/9UcljeVi4FjY4w/BjajOplZwIDWHWM8n+oDdWfgK7R+P38YOJOqi+fEPpfYUmPd9fMVwE9ijAcAV6SU/pYBrHumHFpBkgoxH/vwJUktGPiSVAgDX5IKYeBLUiEMfEkqxLy7LFOaqXpQtR/Vk0enlFbW838IHADclFJa3LD+uVRjNN0KbJ9SyvX8M3n60sMngeuAE1NK58QYjwXOaGp6VUrpTbN/RFJrBr60vuOAlTHGFwKval4YY3wW1UBba4HtgFcClzet9mGqX5l+EvhWjDE1LDsLOK9+PpCjimr+MvBVlBjjc4FvU/3gbS3wW57+cd4NwH4xxhdRDXVxO7B10y4OAjYH/g/Vj6DezIaBf1FKKcUY96Mad+i/Niz7HXBJ/fyRjT8iqXP24as0R1J103yWarjkK3l6UK+LgNuohsA9hmp4jrVN27+ZakyYTwFXAYfUY9Y32irG+BLgZfX0zQ3LTqYaM+lO4AMbfzhS5wx8lWbdQFivprqpyNlUZ/JQBfk3qD4IdqD6Gf1TYoxbUo2F/kvgWVTDNDwf2K+pjUuAa4DFVINv/bxh2enA0vrxzVk4HqljBr6KklI6j+quRhdS9b//kPW7Ns+gGrPmipTS1U2bLwP+C9VAYL8H3l3PP7xpvXdT3fRjh5TSu5uWXZtSuqR+3LCRhyPNiH34KkqM8VDgpVRX0Pya6gY5261bnlK6Nsb4znpZs3XBfhTVjUigGp/+kBjjWMN6P08pJVrbI8b4lvr5vSml/9vdkUgzZ+CrNI9QjSv/QqoRHM+mGnb4KSml05s3qu97+jrgqpTSWQ3zXwu8ixZX9LRxZP0A+HfAwFffOFqmJBXCPnxJKoSBL0mFMPAlqRAGviQVwsCXpEIY+JJUCANfkgrx/wFN+RyVEIiG1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive12_smapes, naive12_time = eval_local_model(\n",
    "    air_train, air_test, NaiveSeasonal, K=12\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdafd472",
   "metadata": {},
   "source": [
    "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6afdf37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8624f38fbf84cdbbd92aa8ad7e4ea69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/301 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ets_smapes, ets_time = eval_local_model(air_train, air_test, ExponentialSmoothing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a33bfc",
   "metadata": {},
   "source": [
    "The median is better for with the naive seasonal. Another model that we can quickly is the `Theta` method which has won the M3 competition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e38a16c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff496c1fac6f4b1589bb986edf9e5e83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/301 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_smapes, theta_time = eval_local_model(air_train, air_test, Theta, theta=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b866091a",
   "metadata": {},
   "source": [
    "And how about ARIMA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "033ab873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa67824da19d4f64a21f9ebf9ef2a9b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/301 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")  # ARIMA generates lots of warnings\n",
    "arima_smapes, arima_time = eval_local_model(air_train, air_test, ARIMA, p=12, d=1, q=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f431b961",
   "metadata": {},
   "source": [
    "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3c82dbfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a05084cf04f4477fb117ea4cb1321b25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/301 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEVCAYAAADjHF5YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbqklEQVR4nO3df5hcZX338ffdZIMiBPBSbJkI2EUpqDU++1VQfhpYVMDGVsIjxQKCDe3j4nMhilaMQBXQghVsqxIuChotgj9XEXwAEQoWL/3uI1UQMIRfshAufiMQYEnu/nHuJbOTmezs7pyZs7k/r+uaK3N+zLk/czL5zsl9ztwnxBgREZFN3x/1OoCIiHSHCr6ISCZU8EVEMqGCLyKSCRV8EZFMqOCLiGRCBV96LoQQQwg7pedfCSEs63UmkU2RCr60LYRwVwjhuRDCyxrm/yoV7R1n2kaM8e9ijJ+e6XamYqrvK4RwSpq/W8P8o0IIa0MIT4YQnggh3BhCODgt2zeEsC4tq3+8pY18m4UQzg8h3B1C+EPa7jtbrPuplG3/jWzv0yGE34QQng8hnNKwrFnOI+uWD4UQPITwbAjhwsmyS7Wo4MtU3QkcNj4RQng9sHnv4nRMW+8rhBCAI4BH0p+NbogxbgFsDZwPXBJC2CYtuy/GuEXD44Y2ss0Ffg/sA2wFfDJtd8eGbP3AEuD+SbZ3O3Ai8KMWyxtzfrV+GfAZ4N/byC0Vo4IvU7WCiYXuSOBr9SukI9KzQgj3hBAeSN00L65b/tEQwv0hhPtCCEc3vPbCEMJn0vNtQgiXhhAeDCE8mp4vqFv3mnS0+rN05HtF41F63bovS69/LITwSAjhuhBC/ed/0veV7AX8CfAh4L0hhHnN2osxrqMoii8G+put064Y41MxxlNijHfFGNfFGC+l+IIaaFj134CPAc9Nsr2vxhgvB/4wjSzfjTF+H3h4qq+V3lPBl6n6OTA/hLBLCGEO8F7g6w3rfBZ4DbAQ2AmoAZ8CCCG8A/gIMAi8GmjZ9UDx+bwA2AHYHlgD/GvDOn8NvB/YFpiXtt3MCcC9wMuBVwCfAOrHFWnnfUHxRfBD4JI0/a5mjYUQ5gIfAJ4EVrZ8h+vX/3gI4dLJ1kvrvoJi/95cN28J8GyM8bJ2tjGJbdMX9Z0hhC+EEF7SgW1KBajgy3SMHw0PArcAo+MLUpfHUuD4GOMjMcY/AKdTFFCAQ4ELYow3xRifAk5p1UiM8eEY43dijE+n7ZxG0a1R74IY4+9ijGsoivDCFpsbozgy3yHGOBZjvC5uOJBUy/eV3tvmFF0m/xFjHAO+zYbdOruHEB4DVlN0Ef1ljPHxtGy79D+M+sdL0nv9bIzx4Fb7oi5DH/AN4KsxxlvTvC0p9vH/nez1bbiVYh/+CbCI4n8R/9yB7UoFzO11AJmVVgD/CbyKDbs9Xk7R9z1S1H4AAjAnPd8OGKlb/+5WjaQC+wXgHcB4P/iWIYQ5Mca1aXp13UueBrZosbkzKb5crki5lscYPzuF9wXwl8DzwPhR9DeAq0IIL48xPpjm/TzGuGeLDPfFGBe0WDap1AW1gqLLZqhu0SnAihjjXdPd9rgY42rW79M7QwgnApcCx85029J7OsKXKYsx3k3Rh3wg8N2GxQ9RdL28Nsa4dXpslU5kQnFC8ZV162+/kaZOAHYGdosxzgf2TvND65e0zPyHGOMJMcY/Bf4C+HAIYb8pvC8ounO2AO4JIawGvgX0UXQrlSr9z+l8iu6o96T/YYzbD/hQCGF1yvVKipO6H+tA0xHViU2G/iJluo4BFqVumRekk5XnAV8IIWwLEEKohRDenla5BDgqhLBrOoI/eSNtbEnx5fFYCOGlk6y7USGEg0MIO6XC+TiwFljX7vsKIdQoCuvBFF0eC4E3AJ+j+dU6nfZlYBfgXan7qt5+wOvqct1HcUT+b802FELoCyG8iOLf/9wQwovSeQtCCG8LIewQCq+kOB8zXPfauem1c4A56bXqKZglVPBlWmKMq2KM3mLxxygu/ft5COEJ4CqKI3XS1SFnA1enda7eSDNnU1zl8hDFSdUfzyDyq1OOJ4EbgC/FGH/auNJG3tffADfGGK+IMa4efwBfBP48hPC6NjJsFza8Dv89ACGET4QQLm/2ohDCDhQFfCGwuu61h6fMDzdkWgs8GmN8Mr3+KyGEr9Rt8jyKL9LDgJPS879Jy94I/BfwVPrzNxRXJI37ZFr/48D70vNPtvHepQKCboAiIpIHHeGLiGRCBV9EJBMq+CIimVDBFxHJRNULfmz3sXr16rbX7fajqtmqmqvK2aqaq8rZqpqrytk6kKupqhf8tq1du3bylXqkqtmqmguqm62quaC62aqaC6qbraxcm0zBFxGRjVPBFxHJhAq+iEgmVPBFRDKhgi8ikgkVfBGRTKjgi4hkQgVfRCQTKvgiIpnQnWqSuvuvTqD7BYjIpkJH+CIimVDBFxHJhAq+iEgmVPBFRDKhgi8ikgkVfBGRTKjgi4hkQgVfRCQTKvgiIplQwRcRyYQKvohIJlTwRUQyUdrgaWb2CuB7wBiwFjgcuAiYk6bPd/cVZbUvIiITlTla5kPAnu6+zsyOAo5J89/p7k+W2K6IiDRRWsF397V1k1sCNwOLgMvM7DHgOHe/u6z2RURkolDmeO9mthA4F9gaOAB40t0fNrN9gBPc/S+avGYpsBRgaGhoYHBwsK22xsbG6Ovrm3bWkZGRpvMHBgamvc1xM81Wlqrmgupmq2ouqG62quaC6mabaa5ardb0Bh+lFvxxZnYosMjd/65unru7TfLStsONjo5Sq9WmG7HUG6DMNFtZqpoLqputqrmgutmqmguqm60DuZoWtNKu0jGzeXWTjwNPm9n8tGxX4NGy2hYRkQ2VedJ2oZmdRXFFzjPA0cDVZrYmLf9giW2LiEiDMk/a/gLYu2H2ZF04IiJSEv3wSkQkEyr4IiKZUMEXEcmECr6ISCZU8EVEMqGCLyKSCRV8EZFMqOCLiGRCBV9EJBMq+CIimVDBFxHJhAq+iEgmVPBFRDKhgi8ikgkVfBGRTKjgi4hkQgVfRCQTKvgiIplQwRcRyYQKvohIJkq7ibmZvQL4HjAGrAUOB/qBfwLWAX/v7r8pq30REZmozCP8h4A93X0f4GvAMcBpwEHAXwOfK7FtERFpEGKMpTdiZsdRfAH8rbsvSvN+7u67N1l3KbAUYGhoaGBwcLCtNsbGxujr65t2xpGRkabzBwYGpr3NcTPNVpaq5oLqZqtqLqhutqrmgupmm2muWq0Wms0vteCb2ULgXGBr4ADgHHd/d1p2PbDI3Z/byCbaDjc6OkqtVpt21hCa7h86sX9mmq0sVc0F1c1W1VxQ3WxVzQXVzdaBXE0LWml9+ADufiOwm5kdCpwEzK9ve5JiLyIiHVTmSdt5dQX9ceBJYK6ZbQ1sCTxSVtsiIrKhMo/wF5rZWRRX6DwDHA28GriMoqvm/5TYtoiINCit4Lv7L4C9G2bfD7y1rDZFRKS1Uvvwq6jVyVkRkU2dfmkrIpIJFXwRkUyo4IuIZEIFX0QkE9mdtJ2qMn+BKyLSTTrCFxHJhAq+iEgmVPBFRDKhgi8ikgkVfBGRTKjgi4hkQgVfRCQTKvgiIplQwRcRyYQKvohIJlTwOyyEsMFjZGRE4/CLSM+p4IuIZEIFX0QkE6WNlmlmbwbOAcaAUeAI4LfpOcBp7n5lWe2LiMhEZQ6P/HtgkbuvMbMzgMXA4+6+b4ltiohIC6UVfHe/v27yOWAdsIWZXUtxlD/k7o+U1b6IiEwUyr6Rh5ntAHwT2BuY7+4Pm9kRwJvc/bgm6y8FlgIMDQ0NDA4OttXO2NgYfX19k643MjIyhfStDQwMtL39/v5+Vq1a1fI1vdLuPuuFqmarai6obraq5oLqZptprlqt1vSywFILvpnNBy4F/tbdb6ubvznwY3ffe5JNtB1udHSUWq026Xqdujyy1X5rtv3h4WEWL15cubtktbvPeqGq2aqaC6qbraq5oLrZOpCraaEr7SodM5tLcWR/qrvfZmbzzGyztHgv4Pay2hYRkQ2VedL2MGA3YJmZLQO+DJxoZk8BzwJHl9i2iIg0KPOk7QpgRcPsi8tqT0RENk4/vBIRyYQKvohIJlTwRUQyoYIvIpIJFXwRkUyUeVnmJk3j24vIbKMjfBGRTKjgi4hkoq2Cb2Z3mNlBddP7mNkV5cUSEZFO22gffhr8bBtgR2AHM9s+LdoH2K/caCIi0kmTHeEfD9xBMWrlvwB3psfJwD3lRhMRkU6a7Cqd3wGXAwcCvwLuoyj+jwLnlhtNREQ6aaMF390vAi4ys5OBb7n7b7sTS0REOq3d6/C/AnzAzI4H5qR50d2PKSeWiIh0WrsF/weAMfEuKhFQwRcRmSXaLfg7AV8HvgQ8X14cEREpS7sFfwWwJfD/3X2sxDwiIlKSdgv+B4AXA0eY2Zo0L7r7VuXEEhGRTmu34D9E0WcvIiKzVFsF3913nOqGzezNwDnAGDAKHAG8m+LHXGuAI9393qluV0REpqetgm9mRzSZHdONylv5PbDI3deY2RnAYuDDFMMyvAlYBhw7xbwiIjJN7XbpXEjzLp2WBd/d76+bfA7YGbjF3Z8DfmZmZ7UbUkREZq7dgn8i6wv+NhTdM9e380Iz2wE4APg48PK6RXNarL8UWAowNDTE4OBgWwHHxsYYHR2ddL3h4eG2ttdJ/f39DA8Pt5Wvm9rdZ71Q1WxVzQXVzVbVXFDdbDPNVavVms5vtw9/wtG4mf03RZfMRqXRNlcAR1EU+Pl1i9e2aGs5sDxNtn2ieHR0tOWbrLdgwYJ2N9kxw8PDLF68mBirdd673X3WC1XNVtVcUN1sVc0F1c1WVq52+/B/0PCaAaBvktfMBb4JnOrut5lZH7CLmc2j+NXur6cXWUREpqPdLp2DG6afoeii2ZjDgN2AZWa2DPgycDZwTXr9kW2nFBGRGWu34L+q7vla4IHJfnGbruBpdlL34jbbFBGRDmrrFofufjfwNuCfgM9THL2LiMgs0u49bT8J/DuwJD0uMLNPlBlMREQ6q62CTzGWzg+B16THpaRLJ0VEZHZot+BvA1zp7re7++3AlWmeiIjMEu2etHXg9DQ+DhTDJPyynEgiIlKGdgv+cRRdOu9L07eneSIiMktstEvHzJaa2Xnp5uU7A68H3gD8J7B3F/JNWwih6aOKZlNWEZm9JjvCPwG4BMDdnwduBjCzUeAjwLmlphMRkY6Z7KTt9sBdTebfA7yy42lERKQ0kxX8h4BDmsw/BHiw83FERKQsk3XpfAf4kJn9GrgqzdsfeC3wxTKDiYhIZ01W8E8CFlKcoH1d3fxr0jIREZklNlrw3f0pYF8zW0QxJHIERtz9p90IJyIindPuDVCuBq4uOYuIiJSo3aEVRERkllPBFxHJhAq+iEgmVPBFRDKhgi8ikol2R8ucMjPbimLc/F2B3d39JjNbCYymVU5z9yvLal9ERCYqreADTwMHAWfWzXvc3fctsU0REWmhtILv7mPAg2ZWP3sLM7uW4ih/yN0fKat9ERGZqMwj/Gb2cPeHzewI4FSa3ETFzJaS7pc7NDTE4OBgWxseGxtjdHT0henh4eGOBO6E/v5+hoeHJ+Sr1yprq/U7pXGfVUlVs1U1F1Q3W1VzQXWzzTRXrVZrOr+rBd/dH05Pv01xY/Rm6ywHlqfJ2O62R0dHJ7zJBQsWTDNl5w0PD7N48WJibP52WmVttX6nNO6zKqlqtqrmgupmq2ouqG62snJ17SodM5tnZpulyb0obpMoIiJdUuoRvpldRjHa5s7A94FDzewp4Fng6DLbFhGRiUot+O5+YMOsz5XZnoiItKYfXomIZEIFX0QkEyr4IiKZUMEXEcmECr6ISCZU8EVEMqGCLyKSCRV8EZFMqOCLiGRCBV9EJBMq+CIimej2ePgyBSGEpvPLHjZZRDZNOsIXEcmECr6ISCZU8EVEMqGCLyKSCRV8EZFM6CqdLmp11Y2ISDfoCF9EJBMq+CIimSitS8fMtgKuBHYFdnf3m8xsCXA8sAY40t3vLat9ERGZqMwj/KeBg4BvA5jZXODDwL7Ap4BlJbYtIiINQtk/0zezC4GzgLXAR9396DT/Bnd/S5P1lwJLAYaGhgYGBwfbamdsbIy+vr4XpkdGRmacvVP6+/tZtWpVx7Y3MDDQke007rMqqWq2quaC6marai6obraZ5qrVak2vEOnmVTrbAE/UTc9ptpK7LweWp8m2v41GR0ep1WovTC9YsGAaEcsxPDzM4sWLO7a9Tn1JN+6zKqlqtqrmgupmq2ouqG62snJ186TtY8D8uum1XWxbRCR73TzCXwnsYmbzAAN+3cW2RUSyV2rBN7PLgIXAzsC5wNnANcAzwJFlti0iIhOVWvDd/cAmsy8us00REWlOP7zKQAih6aNT64vI7KCCLyKSCRV8EZFMqOCLiGRCBV9EJBMaD1/a1urEbdnDc4hIZ+gIX0QkEyr4IiKZUMEXEcmECr6ISCZU8EVEMqGCLyKSCRV8EZFMqOCLiGRCBV9EJBMq+CIimVDBz9jIyEhHxr1vNX7+xh4i0n0q+CIimVDBFxHJRFdHyzSzHYFfAjenWUvc/cFuZhARyVUvhke+1t0P6UG7IiJZ60XB38PMrgOuA05ydw2mLiLSBaGbN68ws80ovmSeBs4DLnf37zSssxRYCjA0NDQwODjY1rbHxsbo6+t7YXpkZKRDqWeuv7+fVatW9TrGBnqZa2BgYKPL2/37bLWdqa7frsZcVVLVbFXNBdXNNtNctVqt9d2KevEYGBg4cGBg4B8nWa9t995774RpoDKP4eHhnmeoWq5O/X22Mt12p5qrSqqaraq5Yqxutg7kalpTu3qVjpltWTe5F3B7N9sXEclZt/vw9zSzz1B06dwJLOty+yIi2epqwXf3y4HLu9mmiIgU9MMr6YlODblQ9tANjdsdH45CZDZSwRcRyYQKvohIJlTwRUQyoYIvIpIJFXyplMaTo7Nt/PxujP+vewzIdKngi4hkQgVfRCQTKvgiIplQwRcRyYQKvohIJnpxAxSRnml1NUss+b4Q02m3V1fe9GofSfl0hC8ikgkVfBGRTKjgi4hkQgVfRCQTKvgi0zCbhjdolXU2vYdWZvt76HZ+FXwRkUyo4IuIZKLr1+Gb2eeAtwJ3AUe7+1i3M4iI5KirR/hm9gag5u57AbcCh3SzfRGRnHW7S+etwBXp+Y+BPbrcvohItrrdpbMNcH96/jjw0sYVzGwpsDRNHuvuy9vZcK1WmzBdtZ+BVy3PuKrmgmpka5ahW7k61U6v92Pjv81Omul7KzNbO7r9d9Ptgv8YMD893wp4pHGFVODbKvIiItK+bnfp/Bewf3r+duBnXW5fRCRbXS347n4j8ICZXQe8FvhON9sXEclZ6HX/noiIdId+eCUikgkVfBGRTKjgi4hkYpO4xWGVhmswszcD5wBjwChwBPDb9BzgNHe/sge5dgR+CdycZi0B9gWOB9YAR7r7vd3OlbK9BTgjTW4H/Ah4IzAHWAuc7+4ruphnK+BKYFdgd3e/ycyW0LCvzOzPKC4hngssc/efdDsbcDcwnDI8D7zf3e82swspLox4CviRu5/ZzVxpn62k4XNfkX22Crg8Ld4c6HP3N5rZKcB7gIeBEXc/oeRczWrFuynxczbrC379cA1mdhLFcA0X9TDS74FF7r7GzM4AFgOPu/u+Pcw07lp3PwTAzOYCHwb2Ad4ELAOO7UUod7+B4suHVKi+T1Hw3+nuT/Yg0tPAQcCZKVOrfXU6cAzwAEUBKb14NWajKBbvc/f7zOztwEeBobTs/e5+UxcyNcsFzT/3Pd9n7r6G9Z+3o4Ad6tb9B3e/tAuZoHmtKPVztil06VRquAZ3vz99oACeA9YBW5jZtWb2H2a2wa+Lu2gPM7vOzE4HXg3c4u7PufvPgD/vYS4AzGwe8GbgOor9dpmZ/cDMdtj4KzvL3cfc/cG6Wa321XbuvtLdnwAeMbOXdTubuz/j7velyfHPG0AEzjOzK9NBUVdzJc0+9z3fZw2WAJfUTX86ZV7UhVyNtWJnSv6cbQoFfxvgifS86XANvZCK1AHAD4E93H0fii+kU3sU6X5gJ2BvYFvgr1i/36DoPum1/YGfuPs6YIm77w18HviX3saa8BmD9fuq/t9PTz976cvyFNbvq4+4+1uA4+jdL9ebfe6rtM+2Bv7Y3W9Js77o7m8EDgXOSfu0GznGa8X1lPw52xQK/mNMMlxDt5nZfGAFcFQ6ung4Lfo2UPrRVjPu/qy7P+XuEfhuyjG/bpW1vcjVYAnwLYDxfebu11L06/fSYzTfV+vq5vX6s7cc+JK7r4QJ++9WIJpZ17/QW3zuq7TPFlOc/wDA3R9Jfz4A3AIsKDtAfa0AHqTkz9mmUPArNVxD6u/9JnCqu99mZvPMbLO0eC/g9h7l2rJuci+KE6O7pHxvBX7di1zjzKyPot/y+jQ9P/25K/BoD6MBrKT5vrrfzPrTvn2puz/Ui3BmdjJwh7tfXDdvfP9tC8xz965+oW/kc1+JfZZM6M6p22ebA3/G+oEeS9FYK+jC52zWn7R19xvNbHy4hnuAs3oc6TBgN2CZmS0DvgycaGZPAc8CR/co155m9hmKE1h3UpwQega4Jv15ZI9yjdsfuDp15wBcbWbj/Zsf7HYYM7sMWEjRr3oucDYb7quTgAsp/ut9ci+ypefLgOtTv/MN7v4PwNdTv/kc4CPdzkVx4v3QJp/7Kuyzc4HvUXTn3Fq32plm9vqU7fS6/vWyNKsVZ1Pi50xDK4iIZGJT6NIREZE2qOCLiGRCBV9EJBMq+CIimVDBFxHJxKy/LFNkpsxsX+CnafKI8YHazOwnwCLgbnffsW79b1GM2XQfsCD9mG18HKDxS+mep7j2/GR3vySN2XJBQ9PD7v7uzr8jkeZU8EUmOhpYYWavAt7WuNDMXkIxENc6il8A70kx9k+9kyh+nXsGxfXwXrfsG8D44Fw9GZ1U8qWCL1lJvzy9iOIHL+sofkI//mO9O4B9zOxPKX7qvhrYumET7wJeDPwzxciG/5sNC/4V7u5mtg/FuCz/q27Z74Cr0vOnZ/6ORNqnPnzJzeEU3TSfB04AbmT9IFVXUPyc/hiKrpmvMXEcEygK/Frgs8BNwHvMrPHf0VZm9hqKoSKg+AX4uFMpxkx5EDhx5m9HpH0q+JKblenP/YB+4GKKI3koCvlXKb4Itqf4OfsL0lgm7wB+BbwEuBb4Y4rxy+tdBdwG7EgxoNkv6pYtBwbT42sdeD8ibVPBl6ykm1vsTjFk754UN5Oo79q8ANiMYkyaWxtevhh4EWAU4xGNj/FzaMN6H6S4wcb27t44DtBKd78qPe6Y4dsRmRL14UtWzOwQiqF6b6e43eMe1A2/7O4rzexY1t8Kst54YX8fxa0DAb5A0a0zVLfeL9zdaW6hmb03PX/U3f/f9N6JyNSp4Etunqa4b+mrKEYkvBiYMNysu29ww5B0X9S3Aze5+zfq5h8A/D1Nruhp4fD0APhvQAVfukajZYqIZEJ9+CIimVDBFxHJhAq+iEgmVPBFRDKhgi8ikgkVfBGRTKjgi4hk4n8ABZoSBn/fIXwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kf_smapes, kf_time = eval_local_model(air_train, air_test, KalmanForecaster, dim_x=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb1a576",
   "metadata": {},
   "source": [
    "### Comparing models\n",
    "\n",
    "Below, we define a function that will be useful to visualise how models compare to each other in terms of median sMAPE, and time required to obtain the forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d47e85f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_models(method_to_elapsed_times, method_to_smapes):\n",
    "    shapes = [\"o\", \"s\", \"*\"]\n",
    "    colors = [\"tab:blue\", \"tab:orange\", \"tab:green\", \"tab:red\", \"tab:purple\"]\n",
    "    styles = list(product(shapes, colors))\n",
    "\n",
    "    plt.figure(figsize=(6, 6), dpi=100)\n",
    "    for i, method in enumerate(method_to_elapsed_times.keys()):\n",
    "        t = method_to_elapsed_times[method]\n",
    "        s = styles[i]\n",
    "        plt.semilogx(\n",
    "            [t],\n",
    "            [np.median(method_to_smapes[method])],\n",
    "            s[0],\n",
    "            color=s[1],\n",
    "            label=method,\n",
    "            markersize=13,\n",
    "        )\n",
    "    plt.xlabel(\"elapsed time [s]\")\n",
    "    plt.ylabel(\"median sMAPE over all series\")\n",
    "    plt.legend(bbox_to_anchor=(1.4, 1.0), frameon=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ef4336ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes = {\n",
    "    \"naive-last\": naive1_smapes,\n",
    "    \"naive-seasonal\": naive12_smapes,\n",
    "    \"Exponential Smoothing\": ets_smapes,\n",
    "    \"Theta\": theta_smapes,\n",
    "    \"ARIMA\": arima_smapes,\n",
    "    \"Kalman Filter\": kf_smapes,\n",
    "}\n",
    "\n",
    "elapsed_times = {\n",
    "    \"naive-last\": naive1_time,\n",
    "    \"naive-seasonal\": naive12_time,\n",
    "    \"Exponential Smoothing\": ets_time,\n",
    "    \"Theta\": theta_time,\n",
    "    \"ARIMA\": arima_time,\n",
    "    \"Kalman Filter\": kf_time,\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times, smapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8df1cd5",
   "metadata": {},
   "source": [
    "### Conclusions so far\n",
    "ARIMA gives the best results, but it is also (by far) the most time-consuming model. The Theta method provides an interesting tradeoff, with good forecasting accuracy and about 50x faster than ARIMA. Can we maybe find a better compromise by considering *global* models - i.e., models that are trained only once, jointly on all time series?\n",
    "\n",
    "## Part 2: Global models on the `air` dataset\n",
    "In this section we will use \"global models\" - that is, models that are fit on multiple series at once. Darts has essentially two kinds of global models:\n",
    "\n",
    "* `RegressionModels` which are wrappers around sklearn-like regression models (Part 2.1).\n",
    "* PyTorch-based models, which offer various deep learning models (Part 2.2).\n",
    "\n",
    "Both models can be trained on multiple series by \"tabularizing\" the data - i.e., taking many (input, output) sub-slices from all the training series, and training machine learning models in a supervised fashion to predict the output based on the input.\n",
    "\n",
    "We start by defining a function `eval_global_model()` which works similarly to `eval_local_model()`, but on global models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea804331",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_global_model(\n",
    "    train_series: List[TimeSeries], test_series: List[TimeSeries], model_cls, **kwargs\n",
    ") -> Tuple[List[float], float]:\n",
    "\n",
    "    start_time = time.time()\n",
    "\n",
    "    model = model_cls(**kwargs)\n",
    "    model.fit(train_series)\n",
    "    preds = model.predict(n=HORIZON, series=train_series)\n",
    "\n",
    "    elapsed_time = time.time() - start_time\n",
    "\n",
    "    smapes = eval_forecasts(preds, test_series)\n",
    "    return smapes, elapsed_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76969990",
   "metadata": {},
   "source": [
    "### Part 2.1: Using Darts `RegressionModel`s.\n",
    "`RegressionModel` in Darts are forecasting models that can wrap around any \"scikit-learn compatible\" regression model to obtain forecasts. Compared to deep learning, they represent good \"go-to\" global models because they typically don't have many hyper-parameters and can be faster to train. In addition, Darts also offers some \"pre-packaged\" regression models such as `LinearRegressionModel` and `LightGBMModel`.\n",
    "\n",
    "We'll now use our function `eval_global_models()`. In the following cells, we will try using some regression models, for example:\n",
    "\n",
    "* `LinearRegressionModel`\n",
    "* `LightGBMModel`\n",
    "* `RegressionModel(some_sklearn_model)`\n",
    "\n",
    "You can refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html) for how to use them.\n",
    "\n",
    "Important parameters are `lags` and `output_chunk_length`. They determine respectively the length of the lookback and \"lookforward\" windows used by the model, and they correspond to the lengths of the input/output subslices used for training. For instance `lags=24` and `output_chunk_length=12` mean that the model will consume the past 24 lags in order to predict the next 12. In our case, because the shortest training series has length 36, we must have `lags + output_chunk_length <= 36`. (Note that `lags` can also be a list of integers representing the individual lags to be consumed by the model instead of the window length)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c09d538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_smapes, lr_time = eval_global_model(\n",
    "    air_train, air_test, LinearRegressionModel, lags=30, output_chunk_length=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d491dac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_smapes, lgbm_time = eval_global_model(\n",
    "    air_train, air_test, LightGBMModel, lags=35, output_chunk_length=1, objective=\"mape\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c56f3f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_smapes, rf_time = eval_global_model(\n",
    "    air_train, air_test, RandomForest, lags=30, output_chunk_length=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d1f2b3",
   "metadata": {},
   "source": [
    "### Part 2.2: Using deep learning\n",
    "Below, we will train an N-BEATS model on our `air` dataset. Again, you can refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html) for documentation on the hyper-parameters.\n",
    "The following hyper-parameters should be a good starting point, and training should take in the order of a minute or two if you're using a (somewhat slow) Colab GPU.\n",
    "\n",
    "During training, you can have a look at the [N-BEATS paper](https://arxiv.org/abs/1905.10437)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "17ca9d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Possible N-BEATS hyper-parameters\n",
    "\n",
    "# Slicing hyper-params:\n",
    "IN_LEN = 30\n",
    "OUT_LEN = 4\n",
    "\n",
    "# Architecture hyper-params:\n",
    "NUM_STACKS = 20\n",
    "NUM_BLOCKS = 1\n",
    "NUM_LAYERS = 2\n",
    "LAYER_WIDTH = 136\n",
    "COEFFS_DIM = 11\n",
    "\n",
    "# Training settings:\n",
    "LR = 1e-3\n",
    "BATCH_SIZE = 1024\n",
    "MAX_SAMPLES_PER_TS = 10\n",
    "NUM_EPOCHS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731992cd",
   "metadata": {},
   "source": [
    "Let's now build, train and predict using an N-BEATS model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9de4c1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-04-07 17:04:05,386] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n",
      "[2022-04-07 17:04:05,386] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 53277 samples.\n",
      "[2022-04-07 17:04:05,403] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n",
      "[2022-04-07 17:04:05,403] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | criterion | SmapeLoss  | 0     \n",
      "1 | stacks    | ModuleList | 525 K \n",
      "-----------------------------------------\n",
      "523 K     Trainable params\n",
      "1.9 K     Non-trainable params\n",
      "525 K     Total params\n",
      "2.102     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3e2791cdc1a4499af7628ff155e7c26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "950e38453a094cb885a6ae213b06017b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 53it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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Xm9mnyixMRERaq6nAp5hL5wfAK9PtWtIEZyIi0h2aDfzdgJvcfYW7rwBuSm0iItIlmj1o68Cn01WsoLiYyc/LKUlERMrQbOCfQTGkc0p6vCK1iYhIlxhzSMfM+s3ssnTx8nnAa4DXAf8OHNqG+rpOCGHUbXBw8Ln7zT5nvPVFRCZjvD38M4GrANz9WeDXAGY2BHwMuLTU6kREpGXGO2i7F/BQnfZHgJe3vBoRESnNeIG/Bji+TvvxwOrWlyMiImUZb0jnauDDZnY3cHNqOxJ4FfClMgsTEZHWGi/wzwbmUxygfXVV+61pmYiIdIkxA9/dnwEON7MFFFMiR2DQ3X/UjuJERKR1mr0Ayi3ALSXXIiIiJWp2agUREelyzX7TdsLMbFeKOXcOAA5y93vM7H5gKK1ygbvfVFb/IiIyWmmBD2wAjgH+qartSXc/vMQ+RUSkgdICP10Ra7WZVTfvbGa3UezlD7j7urL6FxGR0crcw6/nze6+1swWAedTZwI2M+snzbU/MDBAX19f3Q0NDw8zNDRUdxnA8uXLW1Jwoz4GBweb6nfu3LnPtTXaVqNax3p9ZRjvPe0UqrP1uqVW1dmcSqVSt72tge/ua9Pd71FcVKXeOkuBpelhbLStoaGhhi8KYM6cOZOscrQY65fQ7PaXL1/OwoULJ7WtRuuXZbz3tFOoztbrllpV59S07SwdM9vBzHZMDw+hmGJZRETapNQ9fDO7juKbuvOA/wOcaGbPAH8CFpfZt4iIjFZq4Lv70TVNF5bZn4iINNbug7ZZ00VNRGQ66Zu2IiKZUOCLiGRCgS8ikgkFvohIJhT4IiKZ0Fk649CZNSKyvdAevohIJhT4IiKZUOCLiGRCgS8ikgkFvohIJhT4IiKZUOCLiGRCgS8ikgkFvohIJhT4IiKZUOCLiGRCgZ+xEMKo2+DgoOYOEtmOKfBFRDKhwBcRyURp0yOb2a7ATcABwEHufo+ZnQB8BNgInOruj5XVv4iIjFbmHv4G4BjgewBmNhP4KHA4cA6wpMS+RUSkRogxltqBmV0BXARsAT7u7otT+53u/sY66/cD/QADAwO9fX19dbc7PDxMT09Pw34HBwenXHsrzJ07lwceeGBSz+3t7W1JDc2+FyO1tqrfsoz3s+8U3VIndE+tqrM5lUql7tkX7bzi1W7AU1WPZ9Rbyd2XAkvTw4afRkNDQ1QqlYadzZkzZxIltt7y5ctZuHDhpJ7bqg/jZt+LkVrL3gmYqvF+9p2iW+qE7qlVdU5NOw/argdmVT3e0sa+RUSy1849/PuB/c1sB8CAu9vYt4hI9koNfDO7DpgPzAMuBS4GbgU2AaeW2beIiIxWauC7+9F1mq8ss08REamvnUM6MkETneag0w+2isj00jdtRUQyocAXEcmEAl9EJBMKfBGRTCjwRUQyobN0tiPbw8VLGr0GnYEkMnXawxcRyYQCX0QkEwp8EZFMKPBFRDKhwBcRyYTO0pHSjHXWULecdaOzhmR7oj18EZFMKPBFRDKhwBcRyYQCX0QkEzpoK03bHqZuEMmZ9vBFRDKhwBcRyURbh3TMbB/g58CvU9MJ7r66nTWIiORqOsbwb3P346ehXxGRrE1H4L/ZzG4HbgfOdnd9ZVFEpA1CO78ibmY7UnzIbAAuA65396tr1ukH+gEGBgZ6+/r66m5reHiYnp6ehn0NDg62qOqpmTt3Lg888MB0l9GUkVp7e3vrLm/lezrRPqrXr/7ZT7SmVr22RtupNt7vaCfpllpVZ3MqlUrdU+raGvjVzOxo4CB3P2eM1RoWNzQ0RKVSafjETjmFcPny5SxcuHC6y2jKSK2Nfida+Z5OtI/q9at/9hOtqVWvrZn/N+P9jnaSbqlVdTat7i90W8/SMbNdqh4eAqxoZ/8iIjlr9xj+wWb2jxRDOr8HlrS5fxGRbLU18N39euD6dvYpIiIFffFKRCQTCnzZRgih7q1TahocHJx0TZ342hrpplqlOyjwRUQyocAXEcmEAl9EJBMKfBGRTCjwRUQyocAXaaFWnU001b51Vo/Uo8AXEcmEAl9EJBMKfBGRTCjwRUQyMR1XvBLp+oOJraq/He9DM9cYkDxoD19EJBMKfBGRTCjwRUQyocAXEcmEAl9EJBMKfJE2aOW0B2VPoTDR7bdjSod601WMdZvOWiei3fUo8EVEMqHAFxHJRNu/eGVmFwJvAh4CFrv7cLtrEBHJUVv38M3sdUDF3Q8B7gWOb2f/IiI5a/eQzpuAG9P9G4A3t7l/EZFstXtIZzdgZbr/JPDi2hXMrB/oTw8/4O5L622oUqmM2VEnzRPSSbWMp1tqVZ2tVf3/aaI1t+M1Vvcxlf7a9fMYL59GtPv3o92Bvx6Yle7vCqyrXSEFfN2QFxGRyWv3kM5PgCPT/bcCd7S5fxGRbLU18N39LuAPZnY78Crg6nb2LyKSs9AtY4wiIjI1+uKViEgmFPgiIplQ4IuIZKIrr2nbydMzmNkbgC8Cw8AQsAh4J/ARYCNwqrs/Nm0F1jCzk4AvufvuZnYCHVinmR0OLKHYQfkSsBr4HLAV+KC7/2r6qnuemb0A+BowFwjA+4CX0CG1mtmuwE3AAcBB7n5PvZ+5mf0FxanRM4El7v7D6awTeBhYnup5Fvhrd3+40+p093tS+97A74De9B5Pa53Vum4PvwumZ3gUWODuh1J8IC0EPgocDpxDEVwdwcxmACcAj5rZTDqwTjPbCTgTeLu7v8XdrwEuAI4B3gtcOJ311ZgP7Jh+Nz9J8X52Uq0bUi3fAxjjZ/5p4HTgbcD/bHuVNXVS7Dydkv5PXQh8PLV3Wp0jPsHoU86nu87ndF3g0+HTM7j7SnffmB5uBuYBv3X3ze5+B/Da6atuGycB36XY+3wFnVnnGyn2Pn9gZteY2cuALe7+hLs/Qp1va0+jx4BgZoHiW+XP0EG1uvuwu6+uamr0M9/T3e9396eAdWb2kums0903ufvj6eFmit/XjqsTwMz2BSLwSFXztNZZrRsDfzfgqXS/7vQMnSD9WXcU8GOerxdgxvRUNFrauz8RuDI1Vb+v0CF1AnsA+wHvAC4Dzmd0nc+a2Q7TUVgdayj2Ru8F/gX4Ap1bKzT+mVfnQsf8H0vv3XkU7y10Zp1nARfVtHVMnd0Y+OsZZ3qG6WZms4BlwGkU482zqhZvmY6a6jgFuMrdR/aW1tOZda4H7nD3zcAPgb9kdJ0z07JOcBTwrLvPA44DPk/n1gqNf+Zbq9o66f/YUuDL7n5/etxRdZrZXAB3f6hmUcfU2Y0HbX9CMe74DTpweoY0Lvod4Hx3v8/MeoD9096JAXdPa4HPOwD4SzM7heJP+zPozDp/DpyZhknmA78B9jWz2cAudE4YQXGgdm26v4aivpkdWivA/dT/ma9M4fWfwIvdfc10FTjCzM4FHnT3K6uaO63O1wGvMrMbgNcA+5nZkXRQnV0X+O5+l5mNTM/wCNv++TTdTgIOBJaY2RLgEuBi4FZgE3DqtFVWxd3PGrlvZu7uHzSzd9N5da4xs2uA2yjGRhcDFeC69PhvprG8WjcBp5nZbcCOFDsmM+mgWs3sOooPznnApdT/3TwbuIJiiOfcNpcIjK4z3V8C/NjMFgB3uvsnO61O4NJ0wB4zuwK4yN03mdm01zlCUyuIiGSiG8fwRURkEhT4IiKZUOCLiGRCgS8ikgkFvohIJrrutEyRVkuTs/0oPVzk7stS+w+BBcDD7r5P1frfpZjD6XFgjrvH1H4Fz5/a+CywAjjX3a8ys9OAy2u6Xu7u72z9KxKpT4EvMtpiYFmaE+UttQvN7M8oJszaCuwJHAzcXrPa2RTfYv0M8E0z86pl3wKuTfc7YjZSyYcCX7JiZi8Fvk3x5bitwG95/st7DwKHmdmfU0yLsQqYXbOJdwA7Af9M8cWqd7Nt4N/o7m5mh1HMV/Rfq5b9Drg53d8w9Vck0jyN4UtuTqYYpvk8xbTLd/H8pGE3AispprI9lWL6jq01z383xZwznwXuAY5L8+BX29XMXgm8Pj2unjnxfIr5lVZTTKMr0jYKfMnNyMRbR1BcqORKij15KIL86xQfBHtRfB3+OWa2C8Wc5r8A/oxiuof/AhxW08fNwH3APhSTff2satlSoC/dvtGC1yPSNAW+ZMXdr6W4itINFOPvP2T00OblFPPg3Onu99Y8fSHwQoqJxn4PfCi1n1iz3ocoLiqyl7t/qGbZ/e5+c7o9OMWXIzIhGsOXrJjZ8RSzGq4Afk1xAZ09R5a7+/1m9oG0rNZIsJ9CcXETKOa8P87MBqrW+5m7O/XNN7P3pPtPuPv/ndwrEZk4Bb7kZgPFXPX7UswQeSXFVMbPcfeltU9K1y99K3CPu3+rqv0o4IPUOaOngZPTDeCXgAJf2kazZYqIZEJj+CIimVDgi4hkQoEvIpIJBb6ISCYU+CIimVDgi4hkQoEvIpKJ/w/RXGCjr3aDugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reproducibility\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "nbeats_model_air = NBEATSModel(\n",
    "    input_chunk_length=IN_LEN,\n",
    "    output_chunk_length=OUT_LEN,\n",
    "    num_stacks=NUM_STACKS,\n",
    "    num_blocks=NUM_BLOCKS,\n",
    "    num_layers=NUM_LAYERS,\n",
    "    layer_widths=LAYER_WIDTH,\n",
    "    expansion_coefficient_dim=COEFFS_DIM,\n",
    "    loss_fn=SmapeLoss(),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    optimizer_kwargs={\"lr\": LR},\n",
    "    # remove this one if your notebook does run in a GPU environment:\n",
    "    pl_trainer_kwargs={\n",
    "        \"enable_progress_bar\": True,\n",
    "        \"accelerator\": \"gpu\",\n",
    "        \"gpus\": -1,\n",
    "        \"auto_select_gpus\": True,\n",
    "    },\n",
    ")\n",
    "\n",
    "nbeats_model_air.fit(air_train, num_loader_workers=4, epochs=NUM_EPOCHS)\n",
    "\n",
    "# get predictions\n",
    "nb_preds = nbeats_model_air.predict(series=air_train, n=HORIZON)\n",
    "nbeats_elapsed_time = time.time() - start_time\n",
    "\n",
    "nbeats_smapes = eval_forecasts(nb_preds, air_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf484aa",
   "metadata": {},
   "source": [
    "Let's now look again at our errors -vs- time plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "51a1a199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_2 = {\n",
    "    **smapes,\n",
    "    **{\n",
    "        \"Linear Regression\": lr_smapes,\n",
    "        \"LGBM\": lgbm_smapes,\n",
    "        \"Random Forest\": rf_smapes,\n",
    "        \"NBeats\": nbeats_smapes,\n",
    "    },\n",
    "}\n",
    "\n",
    "elapsed_times_2 = {\n",
    "    **elapsed_times,\n",
    "    **{\n",
    "        \"Linear Regression\": lr_time,\n",
    "        \"LGBM\": lgbm_time,\n",
    "        \"Random Forest\": rf_time,\n",
    "        \"NBeats\": nbeats_elapsed_time,\n",
    "    },\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times_2, smapes_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83cae14c",
   "metadata": {},
   "source": [
    "### Conclusions so far\n",
    "So it looks like a linear regression model trained jointly on all series is now providing the best tradeoff between accuracy and speed (about 85x faster than ARIMA for similar accuracy). Linear regression is often the way to go!\n",
    "\n",
    "Our deep learning model N-BEATS is not doing great. Note that we haven't tried to tune it to this problem explicitly, doing so might have produced more accurate results. Instead of spending time tuning it though, in the next part we will see if it can do better by being trained on an entirely different dataset.\n",
    "\n",
    "## Part 3: Training an N-BEATS model on `m4` dataset and use it to forecast `air` dataset\n",
    "Deep learning models often do better when trained on *large* datasets. Let's try to load all 48,000 monthly time series in the M4 dataset and train our model once more on this larger dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a48ae31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading M4 TimeSeries...\n",
      "done. There are 47992 series, with average training length 216.32901316886148\n"
     ]
    }
   ],
   "source": [
    "m4_train, m4_test = load_m4()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7df8178",
   "metadata": {},
   "source": [
    "We can start from the same hyper-parameters as before. \n",
    "\n",
    "With 48,000 M4 training series being on average ~200 time steps long, we would end up with ~10M training samples. With such a number of training samples, each epoch would take too long. So here, we'll limit the number of training samples used per series. This is done when calling `fit()` with the parameter `max_samples_per_ts`. We add a new hyper-parameter `MAX_SAMPLES_PER_TS` to capture this.\n",
    "\n",
    "Since the M4 training series are all slightly longer, we can also use a slightly longer `input_chunk_length`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "83b26117",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing hyper-params:\n",
    "IN_LEN = 36\n",
    "OUT_LEN = 4\n",
    "\n",
    "# Architecture hyper-params:\n",
    "NUM_STACKS = 20\n",
    "NUM_BLOCKS = 1\n",
    "NUM_LAYERS = 2\n",
    "LAYER_WIDTH = 136\n",
    "COEFFS_DIM = 11\n",
    "\n",
    "# Training settings:\n",
    "LR = 1e-3\n",
    "BATCH_SIZE = 1024\n",
    "MAX_SAMPLES_PER_TS = (\n",
    "    10  # <-- new parameter, limiting the number of training samples per series\n",
    ")\n",
    "NUM_EPOCHS = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9564e32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-04-07 17:04:39,614] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n",
      "[2022-04-07 17:04:39,614] INFO | darts.models.forecasting.torch_forecasting_model | Train dataset contains 479920 samples.\n",
      "[2022-04-07 17:04:39,626] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n",
      "[2022-04-07 17:04:39,626] INFO | darts.models.forecasting.torch_forecasting_model | Time series values are 32-bits; casting model to float32.\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | criterion | SmapeLoss  | 0     \n",
      "1 | stacks    | ModuleList | 543 K \n",
      "-----------------------------------------\n",
      "541 K     Trainable params\n",
      "1.9 K     Non-trainable params\n",
      "543 K     Total params\n",
      "2.173     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7db7b713577e434e9536d7f74b1cb2c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<darts.models.forecasting.nbeats.NBEATSModel at 0x7f1e17d40790>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reproducibility\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "\n",
    "nbeats_model_m4 = NBEATSModel(\n",
    "    input_chunk_length=IN_LEN,\n",
    "    output_chunk_length=OUT_LEN,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_stacks=NUM_STACKS,\n",
    "    num_blocks=NUM_BLOCKS,\n",
    "    num_layers=NUM_LAYERS,\n",
    "    layer_widths=LAYER_WIDTH,\n",
    "    expansion_coefficient_dim=COEFFS_DIM,\n",
    "    loss_fn=SmapeLoss(),\n",
    "    optimizer_kwargs={\"lr\": LR},\n",
    "    pl_trainer_kwargs={\n",
    "        \"enable_progress_bar\": True,\n",
    "        \"accelerator\": \"gpu\",\n",
    "        \"gpus\": -1,\n",
    "        \"auto_select_gpus\": True,\n",
    "    },\n",
    ")\n",
    "\n",
    "# Train\n",
    "nbeats_model_m4.fit(\n",
    "    m4_train,\n",
    "    num_loader_workers=4,\n",
    "    epochs=NUM_EPOCHS,\n",
    "    max_samples_per_ts=MAX_SAMPLES_PER_TS,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e684e38",
   "metadata": {},
   "source": [
    "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ab409a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c44763c7ef284e809a618fd71c4c051e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 469it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "preds = nbeats_model_m4.predict(series=air_train, n=HORIZON)  # get forecasts\n",
    "nbeats_m4_elapsed_time = time.time() - start_time\n",
    "\n",
    "nbeats_m4_smapes = eval_forecasts(preds, air_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4b22aba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_3 = {**smapes_2, **{\"N-BEATS (M4-trained)\": nbeats_m4_smapes}}\n",
    "\n",
    "elapsed_times_3 = {\n",
    "    **elapsed_times_2,\n",
    "    **{\"N-BEATS (M4-trained)\": nbeats_m4_elapsed_time},\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times_3, smapes_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc609d5",
   "metadata": {},
   "source": [
    "### Conclusions so far\n",
    "Although it's not the absolute best in terms of accuracy, our N-BEATS model trained on `m4` reaches competitive accuracies. This is quite remarkable because this model has *not* been trained on *any* of the `air` series we've asked it to forecast! The forecasting step with N-BEATS is ~350x faster than the fit-predict step we needed with ARIMA, and about 4x faster than the fit-predict step of linear regression.\n",
    "\n",
    "Just for the fun, we can also inspect manually how this model does on another series -- for example, the monthly milk production series available in `darts.datasets`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "74f643d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "183a8719199e428098d6a8fc8624ef93",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 469it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from darts.datasets import MonthlyMilkDataset\n",
    "\n",
    "series = MonthlyMilkDataset().load().astype(np.float32)\n",
    "train, val = series[:-24], series[-24:]\n",
    "\n",
    "scaler = Scaler(scaler=MaxAbsScaler())\n",
    "train = scaler.fit_transform(train)\n",
    "val = scaler.transform(val)\n",
    "series = scaler.transform(series)\n",
    "pred = nbeats_model_m4.predict(series=train, n=24)\n",
    "\n",
    "series.plot(label=\"actual\")\n",
    "pred.plot(label=\"0-shot forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b779dd8e",
   "metadata": {},
   "source": [
    "### Try training other global models on `m4` and applying on airline passengers\n",
    "Let's now try to train other global models on the M4 dataset in order to see if we can get similar results. Below, we will train some `RegressionModel`s on the full `m4` dataset. This can be quite slow. To have faster training we could use e.g., `random.choices(m4_train, k=5000)` instead of `m4_train` to limit the size of the training set. We could also specify some small enough value for `max_samples_per_ts` in order to limit the number of training samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f25d6af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(42)\n",
    "\n",
    "lr_model_m4 = LinearRegressionModel(lags=30, output_chunk_length=1)\n",
    "lr_model_m4.fit(m4_train)\n",
    "\n",
    "tic = time.time()\n",
    "preds = lr_model_m4.predict(n=HORIZON, series=air_train)\n",
    "lr_time_transfer = time.time() - tic\n",
    "\n",
    "lr_smapes_transfer = eval_forecasts(preds, air_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4e1a8fc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(42)\n",
    "\n",
    "lgbm_model_m4 = LightGBMModel(lags=30, output_chunk_length=1, objective=\"mape\")\n",
    "lgbm_model_m4.fit(m4_train)\n",
    "\n",
    "tic = time.time()\n",
    "preds = lgbm_model_m4.predict(n=HORIZON, series=air_train)\n",
    "lgbm_time_transfer = time.time() - tic\n",
    "\n",
    "lgbm_smapes_transfer = eval_forecasts(preds, air_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7fc449",
   "metadata": {},
   "source": [
    "Finally, let's plot these new results as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ae5b3355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_4 = {\n",
    "    **smapes_3,\n",
    "    **{\n",
    "        \"Linear Reg (M4-trained)\": lr_smapes_transfer,\n",
    "        \"LGBM (M4-trained)\": lgbm_smapes_transfer,\n",
    "    },\n",
    "}\n",
    "\n",
    "elapsed_times_4 = {\n",
    "    **elapsed_times_3,\n",
    "    **{\n",
    "        \"Linear Reg (M4-trained)\": lr_time_transfer,\n",
    "        \"LGBM (M4-trained)\": lgbm_time_transfer,\n",
    "    },\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times_4, smapes_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b722da",
   "metadata": {},
   "source": [
    "Linear regression offers competitive performance too. It is somewhat slower probably only because the inference with N-BEATS is efficiently batched across batches of time series and performed on GPU. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef9f3be",
   "metadata": {},
   "source": [
    "## Part 4 and recap: Use the same model on M3 dataset\n",
    "OK, now, were we lucky with the airline passengers dataset? Let's see by repeating the entire process on a new dataset :) You will see that it actually requires very few lines of code. As a new dataset, we will use `m3`, which contains about 1,400 monthly series from the M3 competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd30aebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building M3 TimeSeries...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc0f5def73af4e8aae58f1fdd6eb0edc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There are 1399 monthly series in the M3 dataset\n",
      "splitting train/test...\n",
      "scaling...\n",
      "done. There are 1399 series, with average training length 100.30092923516797\n"
     ]
    }
   ],
   "source": [
    "m3_train, m3_test = load_m3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ab72e092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b182fceabd24df2b54189aff50f6793",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive1_smapes_m3, naive1_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, NaiveSeasonal, K=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f3ac97e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49228cd52eb849dba2318bd6627f4916",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive12_smapes_m3, naive12_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, NaiveSeasonal, K=12\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "251dd9c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e1a94f8b393461dbf876fb36e8bf0d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ets_smapes_m3, ets_time_m3 = eval_local_model(m3_train, m3_test, ExponentialSmoothing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93469d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64ddb410426444269d02937d414479d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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u3LksXbp0Uuuur2XLlnW13NjYWNfLDsp0qBGmR53WOHWmQ52DrHFkZKRjez9DYAUwq2V6ZfPvqpa22cDD7StGxGLq6wbw/A3v19no6Cjz58+f7Orrpaq6K3vZsmUT/rGGxXSoEaZHndY4daZDncNYYz9D4BZg55zzJkAGrm/a78s5zwV+A8yJiOUTbUCSNLV6GgI554uov2K6I/UQE2dQf730aeDIZrGTgLOpu4dO7mU9kqTV9TQEIuIdHZrPbVvmF8DevaxDktRZz74dJEkafoaAJBXMEJCkghkCklQwQ0CSCmYISFLBDAFJKpghIEkFMwQkqWCGgCQVrJ8DyBUtpdSxvdvRRSWpFzwTkKSCGQKSVDBDQJIKZghIUsEMAUkqmCEgSQUzBCSpYIaAJBXMEJCkghkCklQwQ2DAUkqrPUZHRyccYkKSppohIEkFMwQkqWCGgCQVzBCQpIIZApJUMENAkgpmCEhSwQwBSSqYISBJBTMEJKlghoAkFcwQkKSCGQKSVDBDQJIKNmPQBaizdR1OuqqqHlUiaUPW1xDIOW8H/BS4sWk6FNgPOB54CjgyIu7pZ02SVLJBnAlcFRGHAOScZwAnAPsCrwcWAccNoCZJKlLqZzdCcyZwLXArcDWwBPhIRBzdzL82It7YYb0FwAKAhQsX7jFv3rxJvf6KFSu47bbbJld8n8ydO3dSNe6xxx49qKazsbExZs6c2bfXm6zpUKc1Tp3pUOcgaxwZGenYx9zvM4H7gB2AJ4EzgT8FHmuZv3GnlSJiMbC4mZx0ao2OjjJ//vzJrt4XS5cunVSN/QzzZcuWMTIy0rfXm6zpUKc1Tp3pUOcw1tjXEIiIZ4BnAHLO3wOOAh5vWWRlP+uRpNL19SuiOefNWyb3Bv4d2DnnvEnO+U3A9f2sR5JK1+/uoDfnnD9D3R10B/WF4KeBK5t/j+xzPZJUtH53B10MXNzWfG7zkCT1mb8YlqSCGQKSVDBDQJIKZghIUsEMAUkqmCEgSQVzKOkN3ERDUjv0tCTwTECSiuaZwAZiXW9CI0lgCKiN3UdSWewOkqSCGQKSVDBDQJIKZghIUsEMAUkqmN8OKpRfKZUEnglIUtEMAUkqmCEgSQUzBCSpYIaAJBXMEJCkghkCklQwQ0CSCmYISFLBDAF1JaX0u8fo6Ohq050ekqYHQ0CSCmYISFLBHEBOPbGmLqGJblXprS2l/vNMQJIK5pmA+s4Lx9Lw8ExAkgpmCEhSwewO0rTlhWRp/XkmIEkFMwS0wZnol82T3Y6/htaGbCi6g3LOpwNvAu4Ejo6IscFWpGEyVQdeu4+kFxr4mUDOeTdgJCL2Bm4CDhlwSdKUmswYS56FqF8GHgLUZwCXNs8vAf5ogLVIa7W2wfPW5cA90WB8/XjtQe1zr2oafy+nu36/r8PQHbQFcF/z/FFgTvsCOecFwIJm8riIWDyZF3r3u989LU79rXHqrE+d/drHYX8vR0ZGhrLGTjUNY52tRkZG1rpMv/dhGEJgBTCreT4beLh9geagP6kDvyRpYsPQHfRfwFub528DrhlgLZJUlIGHQERcBzyQc74a2BX47mArkqRypGHvQ5Mk9c7AzwQkSYNjCEhSwQwBSSrYMHxFtC+GdWiKnPMbgL8HxoBlwBHAe4DjgaeAIyPinoEV2CLnfBjw5YjYKud8KENWY855P2AR9YebLwMPAn8HrAI+GBH/PbjqIOe8EfB1YC6QgGOBLRmCGnPOs4HLgF2AvSLihk5/45zzTtRf154BLIqIKwZZJ3AXsLSp5zngAxFx1yDr7PReNu3bAjcDezTv70Dfy3FFnAkM+dAUvwb2j4h9qANqPnACsB/waeqD2sDlnDcGDgV+nXOewZDVmHPeFDgR+OOIeEtEXAB8FjgI+DPg9EHW19gdeFHz3+EnqN/DYanxyaaO7wCs4W98GnAM8Hbgr/teZVud1B+eDm/+/zkd+EjTPsg622sc91FW/wr8oN9LoJAQYIiHpoiI+yLiqWbyWWBH4JcR8WxEXAO8dnDVreYw4HzqT6yvZPhqfCP1J9bv55wvyDn/AbAyIh6JiLvp8Ev0AbgHSDnnRP1L+ScYkhojYiwiHmxpmuhvvHVE3BIRjwEP55y3HGSdEfF0RNzbTD5L/d/nQOvs8F6Sc94eqIC7W5oH+l6OKyUEtgAea553HJpi0JpTxQOBH/F8rQAbD6ai5zVnAe8Fzm2aWt9PGIIagd8HdgDeBZwJnMrqNT6Xc95kEIW1WE79yfUm4B+ALzF8NY6b6G/ceswYmv+XmvftFOr3FYavzo8BX2hrG4oaSwmBFaxlaIpByjnPApYAR1H3Y89qmb1yEDW1ORw4LyLGP2WtYPhqXAFcExHPAlcAr2P1Gmc08wbpQOC5iNgROBj4IsNX47gVdP4br2ppG6b/lxYDX4mIW5rpoakz5zwXICLubJs1FDWWcmH4v6j7N89hyIamaPpevw2cGhG/yjnPBHZuPtlk4PqBFljbBXhdzvlw6m6CDzN8Nf4UOLHpatkd+AWwfc75pcDmDMfBKgEPNc+XU9c1Y8hqHHcLnf/G9zUHtd8AcyJi+aAKHJdzPhm4PSLObWkepjp3A3bNOV8CvAbYIef81mGpsYgQiIjrcs7jQ1PczQtPywbpMGBPYFHOeRHwj8AZwJXA08CRA6usEREfG3+ec46I+GDO+X0MV43Lc84XAFdR970eDYwAFzXT/3uA5Y27DDgq53wV8CLqDyYzGJIac84XUQfojsDX6Pzf4UnA2dTdQyf3uURg9Tqb54uAH+Wc9weujYhPDLrO9vey+TIAOeezgS9ExNM554G/l+CwEZJUtFKuCUiSOjAEJKlghoAkFcwQkKSCGQKSVLAiviIqratmMLofNpNHRMSSpv0KYH/grojYrmX586nHpLoXeHlEVE372Tz/9crngFuBkyPivJzzUcBZbS+9NCLeM/V7JHVmCEhrdzSwpBn/5S3tM3POL6EeMGwVsDXwZuDqtsVOov4V7t8C38g5R8u8bwIXNs8HPhqrymIIqHg555cB36L+0d4q4Jc8/4PC24F9c86voB7W437gpW2beBewKfB/qH8A9j5eGAKXRkTknPelHofpf7bMuxm4vHn+5PrvkdQ9rwlI8OfUXTxfpB6O+jqeHzDtUuA+6iF/j6QeemRV2/rvox5b53PADcDBzb0DWs3OOb8KeH0z3Tqa5KnUY0Y9SD3csNQ3hoBUj5MDcAD1DV/Opf7ED/XB/Z+pw2Eb6p/5/07OeXPq8eB/BryEetiK/wHs2/YalwO/ArajHujsJy3zFgPzmsc5U7A/UtcMARUvIi6kvkvVJdT9+VewelfpWdRj/VwbETe1rT4feDH1IGt3AB9q2t/bttyHqG/Qsk1EfKht3i0RcXnzuH09d0daJ14TUPFyzodQj/R4K3Aj9U2Hth6fHxG35JyPa+a1Gz/YH059kxio7xNwcM55YctyP4mIoLPdc87vb54/EhH/Mbk9kdadISDVF2MPBranHjHzXOqhnn8nIha3r9TcS/ZtwA0R8c2W9gOBD9Lhm0QT+PPmAfBzwBBQ3ziKqCQVzGsCklQwQ0CSCmYISFLBDAFJKpghIEkFMwQkqWCGgCQV7P8DiA3sYrvL1sEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_smapes_m3, theta_time_m3 = eval_local_model(m3_train, m3_test, Theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b9edb800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4a654e9f89f47bf86d07e1e4097aea1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")  # ARIMA generates lots of warnings\n",
    "\n",
    "# Note: using q=1 here generates errors for some series, so we use q=0\n",
    "arima_smapes_m3, arima_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, ARIMA, p=12, d=1, q=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "76e56c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "905d3d78ba4140dbacd0f86dcf03408f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nfe5XkrSdbTUccs6/BQ6LiMNphukuwFjO+eszONZ64MCI2AkI4Jp2/oaIWAbcCizp7lKSJM29Xm/2czlweb87j4iv0Jx57E/zyaazaM467gFWtqudRnMb0kXA6f0eQ5K0/fX6JbgZyTm/ZIrZ53et8wPgkEHWIUnqT69jK0mSdiCGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkioDvYe0HpRSmnJ+KWWOK5GkbfPMQZJUMRwkSRW7lYbM7iZJ85FnDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDiMmpfTANDY29sBjSdqeDAdJUmVOb/YTEfsA3wG+3846FjgMOAW4G1iZc755LmuSJNWGcSe4b+ScjwGIiMXAm4FDgWcAa4DXD6EmSVKHYYTDcyPiSuBK4FzgupzzfcBVEfGhIdQjSeoy1+GwAdgPuAv4JPDHwJ0dyxdNtVFErAJWAaxevZrly5fP6ODLli1j3bp1M9p2ro2Pj085v7P+zvZMt/6omZiYWDBtmWSbRsNCb9PSpUv72nZOwyHnfC9wL0BEfBE4AfhNxyqbp9luLbC2fVpmevyxsTFWrFgx083nnXXr1j3QnlJm/GOZV8bHx/v+I57vbNNosE0PNaefVoqIXTqeHgL8H+DAiNgpIp4DXDOX9UiSpjbX3UrPi4i/oulW+gnNBeh7gCvaf1fOcT2SpCnMdbfSRcBFXbPPbyfNoem+OLdQuqckzY5fgpMkVQwHSVLFcJAkVQwHSVJlGN+Q1gB4gVnS9uSZgySpYjhIkiqGgySpYjhIkiqGgySpYjhIkip+lFUP4UdiJYHhsOBN92IvSVtjt5IkqeKZg3pid5O0YzEcNCuGhrQw2a0kSaoYDpKkiuEgSaoYDhqIlNK009a2GRsb63l9SYNjOEiSKoaDJKliOEiSKoaDJKliOEiSKoaDJKni8Bmac348VZr/PHOQJFU8c9C81+/gfg4GKM2eZw6SpIrhIEmqGA6SpIrhIEmqeEFaI8uPxEqD45mDJKnimYN2eH70VaoZDlKfDBPtCAwH7TD6vUbhNQ3tyLzmIEmqzIszh4g4E3gO8FPgxJzzxHArkvo33ZnGzTffPMeVSLM39DOHiHgasDTnfAjwQ+CYIZckqUcppSmn7bW+hmfo4UBzxnBx+/irwHOHWIu03Y2NjU37ojisaTq9bj/Zpu1lJrVu72MP6vc003qGHaLzoVtpd2BD+/gOYEn3ChGxCljVPn19znntTA708pe/fMF9omShtQds0zD1U+dM2jQffw6dNc2H+rZnDUuXLp3xtvMhHG4Hdm0f7wZs6l6hDYMZBYIkqX/zoVvp34AXtY9fDFw1xFokScyDcMg5fw/4RURcCTwF+MJwK5IkpfnQxyZJml+GfuYgSZp/DAdJUsVwkCRV5sNHWQduoQzPERHPBP4WmADGgeOBVwCnAHcDK3POIzlWQ0QcB3w05/zYiDiWEW9TRBwGrKF5A/ZR4DbgA8AW4A055/8YXnX9i4iHAZ8ClgEJOBnYgxFrU0TsBlwCHAQ8O+d87VR/bxFxAM3H5xcDa3LOlw2t6G3obhNwI7COpvb7gT/LOd/Yb5sW/JnDAhue42fA4Tnn59ME3QrgzcBhwLtoXoxGTkQsAo4FfhYRixnxNkXEzsCpwB/lnF+Qc/4S8D7gpcCfAGcOs74ZOhh4RPv/6C9ofkej2Ka7aGr+PMBW/t7OAE4CjgTeO+dV9uchbaJ58/ja9nXiTOCt7fy+2rTgw4EFNDxHznlDzvnu9ul9wP7AdTnn+3LOVwFPHV51s3Ic8Dmad6BPYvTb9Ic070K/HBFfiojfBTbnnH+Vc76JKUYBGAE3AykiEs2oBr9lBNuUc57IOd/WMWu6v7c9c87rc853ApsiYo85L7ZH3W3KOd+Tc/55+/Q+mv9X0GebdoRw2B24s3085fAcoyYi9gaOAL7Jg20DWDScimauPWt4FXB+O6vz9wUj2Cbg8cB+wMuATwLv4aFtuj8idhpGYbOwkeYd6Q+BjwEfYfTbBNP/vXW+No7k60b7+3g3ze8L+mzTjhAOt7ON4TlGSUTsCpwLnEDTj71rx+LNw6hpll4LXJBznnx3czuj36bbgatyzvcBlwFP56FtWtwuGyVHAPfnnPcHjgY+zOi3Cab/e9vSMW9UXzfWAh/POa9vn/fVph3hgvS/0fQpfpoRH56j7R/9Z+A9OecfRcTDgQPbdwgBXDPUAmfmIODpEfFamlP8NzH6bfoOcGrbBXMw8ANg34h4DLALo/lCk4Bfto830rRj8Yi3CWA9U/+9bYiIZcCtwJKc88ZhFTgTEXE6cEPO+fyO2X21acGHQ875exExOTzHTcCHhl3TLBwHPAtYExFrgL8DzgKuAO4BVg6tshnKOb998nFE5JzzGyLi1Yx2mzZGxJeAbwAFOBFYCnylff7fh1jeTF0CnBAR3wAeQfOGazEj2KaI+ApNaO8PfIKp/w+dBpxD0810+hyX2LfONrWP1wDfjIjDgatzzn9Bn21y+AxJUmVHuOYgSeqT4SBJqhgOkqSK4SBJqhgOkqTKgv8oqzQT7cB5X2+fHp9zPredfxlwOHBjznmfjvU/RzNu18+BJ+ScSzv/HB78eOT9wPXA6TnnCyLiBODsrkOvyzm/Yvu3SOqP4SBt24nAuRGxL/CC7oUR8Siagc+2AHsCzwOu7FrtNJpv4/41cF5E5I5lnwEubB+P3Ai0WpgMB+3wIuJxwGdpvmC4BbiOB78seQNwaEQ8kWbIkluAx3Tt4mXAzsDf0Hw57NXU4XBxzjlHxKE0Y0n9l45lPwYubR/fNfsWSbPnNQcJ/pSmq+jDNENtf48HB2C7GNhAM9TxSpphWLZ0bf9qmjF53g9cCxzd3v+g024R8WTgGe3zmzqWvYdmnKzbgLfNvjnS7BkOUjO+DsALaW5mcz7NGQI0L/r/SBMae9EMP/CAiNiFZnz87wKPohky4z8Dh3Yd41LgR8A+NIOhfbtj2VpgeTt9eju0R5o1w0E7vJzzhTR30PoqzfWCy3hol+vZNOMJXZ1z/mHX5iuA36EZtO0nwBvb+a/qWu+NNDeU2Svn/MauZetzzpe20w2zbI60XXjNQTu8iDgGeBrNJ4m+T3NDqD0nl+ec10fE69tl3SZD4LU0N8CB5l4HR0fE6o71vp1zzkzt4Ih4Tfv4Vznnr82sJdL2YzhIzUXgo4F9aUbmPJ9mWOoH5JzXdm/U3rv3xcC1OefPdMw/AngDU3yyaRp/2k4A/w4YDho6R2WVJFW85iBJqhgOkqSK4SBJqhgOkqSK4SBJqhgOkqSK4SBJqvx/qLrjPGM07jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kf_smapes_m3, kf_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, KalmanForecaster, dim_x=12\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bb2eff2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_smapes_m3, lr_time_m3 = eval_global_model(\n",
    "    m3_train, m3_test, LinearRegressionModel, lags=30, output_chunk_length=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39a81a9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_smapes_m3, lgbm_time_m3 = eval_global_model(\n",
    "    m3_train, m3_test, LightGBMModel, lags=35, output_chunk_length=1, objective=\"mape\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "355f8190",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9afcb447a9024770bc0ccd86ceca1be5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 469it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get forecasts with our pre-trained N-BEATS\n",
    "\n",
    "start_time = time.time()\n",
    "preds = nbeats_model_m4.predict(series=m3_train, n=HORIZON)  # get forecasts\n",
    "nbeats_m4_elapsed_time_m3 = time.time() - start_time\n",
    "\n",
    "nbeats_m4_smapes_m3 = eval_forecasts(preds, m3_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3658e9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get forecasts with our pre-trained linear regression model\n",
    "\n",
    "start_time = time.time()\n",
    "preds = lr_model_m4.predict(series=m3_train, n=HORIZON)  # get forecasts\n",
    "lr_m4_elapsed_time_m3 = time.time() - start_time\n",
    "\n",
    "lr_m4_smapes_m3 = eval_forecasts(preds, m3_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b984116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get forecasts with our pre-trained LightGBM model\n",
    "\n",
    "start_time = time.time()\n",
    "preds = lgbm_model_m4.predict(series=m3_train, n=HORIZON)  # get forecasts\n",
    "lgbm_m4_elapsed_time_m3 = time.time() - start_time\n",
    "\n",
    "lgbm_m4_smapes_m3 = eval_forecasts(preds, m3_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cc711d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_m3 = {\n",
    "    \"naive-last\": naive1_smapes_m3,\n",
    "    \"naive-seasonal\": naive12_smapes_m3,\n",
    "    \"Exponential Smoothing\": ets_smapes_m3,\n",
    "    \"ARIMA\": arima_smapes_m3,\n",
    "    \"Theta\": theta_smapes_m3,\n",
    "    \"Kalman filter\": kf_smapes_m3,\n",
    "    \"Linear Regression\": lr_smapes_m3,\n",
    "    \"LGBM\": lgbm_smapes_m3,\n",
    "    \"N-BEATS (M4-trained)\": nbeats_m4_smapes_m3,\n",
    "    \"Linear Reg (M4-trained)\": lr_m4_smapes_m3,\n",
    "    \"LGBM (M4-trained)\": lgbm_m4_smapes_m3,\n",
    "}\n",
    "\n",
    "times_m3 = {\n",
    "    \"naive-last\": naive1_time_m3,\n",
    "    \"naive-seasonal\": naive12_time_m3,\n",
    "    \"Exponential Smoothing\": ets_time_m3,\n",
    "    \"ARIMA\": arima_time_m3,\n",
    "    \"Theta\": theta_time_m3,\n",
    "    \"Kalman filter\": kf_time_m3,\n",
    "    \"Linear Regression\": lr_time_m3,\n",
    "    \"LGBM\": lgbm_time_m3,\n",
    "    \"N-BEATS (M4-trained)\": nbeats_m4_elapsed_time_m3,\n",
    "    \"Linear Reg (M4-trained)\": lr_m4_elapsed_time_m3,\n",
    "    \"LGBM (M4-trained)\": lgbm_m4_elapsed_time_m3,\n",
    "}\n",
    "\n",
    "plot_models(times_m3, smapes_m3)"
   ]
  },
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   "source": [
    "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series. ARIMA performs best but is about 170x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n",
    "\n",
    "## Conclusions\n",
    "Transfer learning and meta learning is definitely an interesting phenomenon that is at the moment under-explored in time series forecasting. When does it succeed? When does it fail? Can fine tuning help? When should it be used? Many of these questions still have to be explored but we hope to have shown that doing so is quite easy with Darts models.\n",
    "\n",
    "Now, which method is best for your case? As always, it depends. If you're dealing mostly with isolated series that have a sufficient history, classical methods such as ARIMA will get you a long way. Even on larger datasets, if compute power is not too much an issue, they can represent interesting out-of-the-box options. On the other hand if you're dealing with larger number of series, or series of higher dimensionalities, ML methods and global models will often be the way to go. They can capture patterns across wide ranges of different time series, and are in general faster to run. Don't under-estimate linear regression based models in this category! If you have reasons to believe you need to capture more complex patterns, or if inference speed is *really* important for you, give deep learning methods a shot. N-BEATS has proved its worth for meta-learning [1], but this can potentially work with other models too.\n",
    "\n",
    "[1] Oreshkin et al., \"Meta-learning framework with applications to zero-shot time-series forecasting\", 2020, https://arxiv.org/abs/2002.02887"
   ]
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